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1102641b323d87404d4f1cd6b36517af7ec6ccf63aa5b58eaee86abd60fa0f31
@pytest.mark.serial def test_delete_zone_twice(shared_zone_test_context): '\n Test deleting a zone with deleted status returns 404\n ' client = shared_zone_test_context.ok_vinyldns_client result_zone = None try: zone_name = f'one-time{shared_zone_test_context.partition_id}' zone = {'name': zone_name, 'email': '[email protected]', 'adminGroupId': shared_zone_test_context.ok_group['id'], 'connection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}, 'transferConnection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}} result = client.create_zone(zone, status=202) result_zone = result['zone'] client.wait_until_zone_active(result_zone['id']) client.delete_zone(result_zone['id'], status=202) client.wait_until_zone_deleted(result_zone['id']) client.delete_zone(result_zone['id'], status=404) result_zone = None finally: if result_zone: client.abandon_zones([result_zone['id']], status=202)
Test deleting a zone with deleted status returns 404
modules/api/src/test/functional/tests/zones/delete_zone_test.py
test_delete_zone_twice
Jay07GIT/vinyldns
0
python
@pytest.mark.serial def test_delete_zone_twice(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client result_zone = None try: zone_name = f'one-time{shared_zone_test_context.partition_id}' zone = {'name': zone_name, 'email': '[email protected]', 'adminGroupId': shared_zone_test_context.ok_group['id'], 'connection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}, 'transferConnection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}} result = client.create_zone(zone, status=202) result_zone = result['zone'] client.wait_until_zone_active(result_zone['id']) client.delete_zone(result_zone['id'], status=202) client.wait_until_zone_deleted(result_zone['id']) client.delete_zone(result_zone['id'], status=404) result_zone = None finally: if result_zone: client.abandon_zones([result_zone['id']], status=202)
@pytest.mark.serial def test_delete_zone_twice(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client result_zone = None try: zone_name = f'one-time{shared_zone_test_context.partition_id}' zone = {'name': zone_name, 'email': '[email protected]', 'adminGroupId': shared_zone_test_context.ok_group['id'], 'connection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}, 'transferConnection': {'name': 'vinyldns.', 'keyName': VinylDNSTestContext.dns_key_name, 'key': VinylDNSTestContext.dns_key, 'primaryServer': VinylDNSTestContext.name_server_ip}} result = client.create_zone(zone, status=202) result_zone = result['zone'] client.wait_until_zone_active(result_zone['id']) client.delete_zone(result_zone['id'], status=202) client.wait_until_zone_deleted(result_zone['id']) client.delete_zone(result_zone['id'], status=404) result_zone = None finally: if result_zone: client.abandon_zones([result_zone['id']], status=202)<|docstring|>Test deleting a zone with deleted status returns 404<|endoftext|>
089a1ad491a8f31f81ced3d08a8ab90b2696d5952fe3f411f0d36dd6268bd2b5
def test_delete_zone_returns_404_if_zone_not_found(shared_zone_test_context): '\n Test deleting a zone returns a 404 if the zone was not found\n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('nothere', status=404)
Test deleting a zone returns a 404 if the zone was not found
modules/api/src/test/functional/tests/zones/delete_zone_test.py
test_delete_zone_returns_404_if_zone_not_found
Jay07GIT/vinyldns
0
python
def test_delete_zone_returns_404_if_zone_not_found(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('nothere', status=404)
def test_delete_zone_returns_404_if_zone_not_found(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('nothere', status=404)<|docstring|>Test deleting a zone returns a 404 if the zone was not found<|endoftext|>
7d0125b2273da0ae00a5f869cf10ca40ebb75d675e3dfd671e1e85e0fbf1b43f
def test_delete_zone_no_authorization(shared_zone_test_context): '\n Test deleting a zone without authorization\n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('1234', sign_request=False, status=401)
Test deleting a zone without authorization
modules/api/src/test/functional/tests/zones/delete_zone_test.py
test_delete_zone_no_authorization
Jay07GIT/vinyldns
0
python
def test_delete_zone_no_authorization(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('1234', sign_request=False, status=401)
def test_delete_zone_no_authorization(shared_zone_test_context): '\n \n ' client = shared_zone_test_context.ok_vinyldns_client client.delete_zone('1234', sign_request=False, status=401)<|docstring|>Test deleting a zone without authorization<|endoftext|>
8d20b1e7de817628462edf5e4e96faa3deb6919c0c6666e3c03b86ff9ac41b3b
def __init__(self, service: str, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = f'There was an issue with the {service} API.' if reason: error += f' Reason: {reason}.' super().__init__(error) self.error = error
Parameters ---------- reason: str The reason for the Error. Defaults to None.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, service: str, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = f'There was an issue with the {service} API.' if reason: error += f' Reason: {reason}.' super().__init__(error) self.error = error
def __init__(self, service: str, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = f'There was an issue with the {service} API.' if reason: error += f' Reason: {reason}.' super().__init__(error) self.error = error<|docstring|>Parameters ---------- reason: str The reason for the Error. Defaults to None.<|endoftext|>
de0b05f8dc6485475519625c985d346de577cf09002a3f2073c722685c1b229e
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Hypixel', reason)
Parameters ---------- reason: str The reason for the Error. Defaults to None.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Hypixel', reason)
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Hypixel', reason)<|docstring|>Parameters ---------- reason: str The reason for the Error. Defaults to None.<|endoftext|>
a06c35f2692d590c4480d0a0968ea087d685c6f4d2ced9d514b1f67c02ffe7c9
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Crafatar', reason)
Parameters ---------- reason: str The reason for the Error. Defaults to None.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Crafatar', reason)
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Crafatar', reason)<|docstring|>Parameters ---------- reason: str The reason for the Error. Defaults to None.<|endoftext|>
71973f2fc340d7befa86b536e5469e41195808d87f0394f768651a173ee528ca
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Mojang', reason)
Parameters ---------- reason: str The reason for the Error. Defaults to None.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Mojang', reason)
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' super().__init__('Mojang', reason)<|docstring|>Parameters ---------- reason: str The reason for the Error. Defaults to None.<|endoftext|>
1500a69248777ef192cd88a6d173f2377b9c73936e839881949a923d70895f59
def __init__(self, retry_after: datetime) -> None: '\n Parameters\n ----------\n retry_after: datetime\n The time when the API will be available again for fetching.\n ' error = f"The rate-limit for the Hypixel API was hit. Try again after{retry_after.strftime('%Y-%m-%d %H:%M:%S')}." super().__init__(error) self.error = error
Parameters ---------- retry_after: datetime The time when the API will be available again for fetching.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, retry_after: datetime) -> None: '\n Parameters\n ----------\n retry_after: datetime\n The time when the API will be available again for fetching.\n ' error = f"The rate-limit for the Hypixel API was hit. Try again after{retry_after.strftime('%Y-%m-%d %H:%M:%S')}." super().__init__(error) self.error = error
def __init__(self, retry_after: datetime) -> None: '\n Parameters\n ----------\n retry_after: datetime\n The time when the API will be available again for fetching.\n ' error = f"The rate-limit for the Hypixel API was hit. Try again after{retry_after.strftime('%Y-%m-%d %H:%M:%S')}." super().__init__(error) self.error = error<|docstring|>Parameters ---------- retry_after: datetime The time when the API will be available again for fetching.<|endoftext|>
f4e2115b021b832445cd1e6d96542669ebca6fcee175453598925b01ecefb37f
def __init__(self, reason: t.Optional[str]=None, user: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the error.\n user: t.Optional[str]\n The user not found when searched for.\n ' error = 'Player not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error self.user = user
Parameters ---------- reason: str The reason for the error. user: t.Optional[str] The user not found when searched for.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, reason: t.Optional[str]=None, user: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the error.\n user: t.Optional[str]\n The user not found when searched for.\n ' error = 'Player not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error self.user = user
def __init__(self, reason: t.Optional[str]=None, user: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the error.\n user: t.Optional[str]\n The user not found when searched for.\n ' error = 'Player not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error self.user = user<|docstring|>Parameters ---------- reason: str The reason for the error. user: t.Optional[str] The user not found when searched for.<|endoftext|>
3964cea8338e8d90048f8bd7df8bea356b0380ba529696ea5a0085eefcbcb5b0
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = 'Guild not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error
Parameters ---------- reason: str The reason for the Error. Defaults to None.
hypixelio/exceptions/exceptions.py
__init__
GrandMoff100/HypixelIO
0
python
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = 'Guild not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error
def __init__(self, reason: t.Optional[str]=None) -> None: '\n Parameters\n ----------\n reason: str\n The reason for the Error. Defaults to None.\n ' error = 'Guild not found.' if reason: error += f' {reason}.' super().__init__(error) self.error = error<|docstring|>Parameters ---------- reason: str The reason for the Error. Defaults to None.<|endoftext|>
d06d16036854a3bda9d4b38a0162a04fc77cea7b559c5d1b92f33716d0448c51
def __init__(self): '\n Init method.\n ' super().__init__()
Init method.
HybridNet/submodel.py
__init__
GANWANSHUI/HybridNet
0
python
def __init__(self): '\n \n ' super().__init__()
def __init__(self): '\n \n ' super().__init__()<|docstring|>Init method.<|endoftext|>
63e2621e539c4f7081a967d70552be2c044ba1d6cc91d5e0d4084da8018a7a46
def forward(self, input): '\n Forward pass of the function.\n ' return (input * torch.tanh(F.softplus(input)))
Forward pass of the function.
HybridNet/submodel.py
forward
GANWANSHUI/HybridNet
0
python
def forward(self, input): '\n \n ' return (input * torch.tanh(F.softplus(input)))
def forward(self, input): '\n \n ' return (input * torch.tanh(F.softplus(input)))<|docstring|>Forward pass of the function.<|endoftext|>
74bea9d4c0fe8616e0d76b9f54858f5f802b489856383f056798ad6af7d18d35
def largest_indices(ary, n): 'Returns the n largest indices from a numpy array.' flat = ary.flatten() indices = np.argpartition(flat, (- n))[(- n):] indices = indices[np.argsort((- flat[indices]))] return np.unravel_index(indices, ary.shape)
Returns the n largest indices from a numpy array.
VPR_Techniques/CoHOG_Python/CoHOG.py
largest_indices
oravus/VPR-Bench
30
python
def largest_indices(ary, n): flat = ary.flatten() indices = np.argpartition(flat, (- n))[(- n):] indices = indices[np.argsort((- flat[indices]))] return np.unravel_index(indices, ary.shape)
def largest_indices(ary, n): flat = ary.flatten() indices = np.argpartition(flat, (- n))[(- n):] indices = indices[np.argsort((- flat[indices]))] return np.unravel_index(indices, ary.shape)<|docstring|>Returns the n largest indices from a numpy array.<|endoftext|>
353c9ee2915c34cea6a446c28f8c9be428980e7f46b7a4aba61f227862c09af3
def compute_map_features(ref_map): 'INPUT: reference list of images to be matched.' 'OUTPUT: Feature descriptors of all reference images to be matched.' ref_desc = [] for ref in range(len(ref_map)): img_1 = cv2.cvtColor(ref_map[ref], cv2.COLOR_BGR2GRAY) if (img_1 is not None): img_1 = cv2.resize(img_1, (magic_height, magic_width)) (height, width, angle_unit) = initialize(img_1, cell_size, bin_size) vector_1 = extract() vector_1 = np.asfortranarray(vector_1.transpose(), dtype=np.float32) ref_desc.append(vector_1) print('Reference images descriptors computed!') return ref_desc
INPUT: reference list of images to be matched.
VPR_Techniques/CoHOG_Python/CoHOG.py
compute_map_features
oravus/VPR-Bench
30
python
def compute_map_features(ref_map): 'OUTPUT: Feature descriptors of all reference images to be matched.' ref_desc = [] for ref in range(len(ref_map)): img_1 = cv2.cvtColor(ref_map[ref], cv2.COLOR_BGR2GRAY) if (img_1 is not None): img_1 = cv2.resize(img_1, (magic_height, magic_width)) (height, width, angle_unit) = initialize(img_1, cell_size, bin_size) vector_1 = extract() vector_1 = np.asfortranarray(vector_1.transpose(), dtype=np.float32) ref_desc.append(vector_1) print('Reference images descriptors computed!') return ref_desc
def compute_map_features(ref_map): 'OUTPUT: Feature descriptors of all reference images to be matched.' ref_desc = [] for ref in range(len(ref_map)): img_1 = cv2.cvtColor(ref_map[ref], cv2.COLOR_BGR2GRAY) if (img_1 is not None): img_1 = cv2.resize(img_1, (magic_height, magic_width)) (height, width, angle_unit) = initialize(img_1, cell_size, bin_size) vector_1 = extract() vector_1 = np.asfortranarray(vector_1.transpose(), dtype=np.float32) ref_desc.append(vector_1) print('Reference images descriptors computed!') return ref_desc<|docstring|>INPUT: reference list of images to be matched.<|endoftext|>
b63145126a9bf35e065bafd1c05fe6192c24a4cc308ae943958963a28472af4e
def perform_VPR(query_info, ref_map_features): "INPUT: Query desc and reference list of images' features to be matched." 'OUTPUT: Matching Score and Best Matched Image.' vector_2 = query_info[0] regional_goodness = query_info[1] confusion_vector = np.zeros(len(ref_map_features), dtype=np.float32) ref_desc = ref_map_features for ref in range(len(ref_map_features)): score = conv_match_dotproduct(vector_2.astype('float64'), ref_desc[ref].astype('float64'), regional_goodness, total_no_of_regions) confusion_vector[ref] = score return (np.amax(confusion_vector), np.argmax(confusion_vector), confusion_vector)
INPUT: Query desc and reference list of images' features to be matched.
VPR_Techniques/CoHOG_Python/CoHOG.py
perform_VPR
oravus/VPR-Bench
30
python
def perform_VPR(query_info, ref_map_features): 'OUTPUT: Matching Score and Best Matched Image.' vector_2 = query_info[0] regional_goodness = query_info[1] confusion_vector = np.zeros(len(ref_map_features), dtype=np.float32) ref_desc = ref_map_features for ref in range(len(ref_map_features)): score = conv_match_dotproduct(vector_2.astype('float64'), ref_desc[ref].astype('float64'), regional_goodness, total_no_of_regions) confusion_vector[ref] = score return (np.amax(confusion_vector), np.argmax(confusion_vector), confusion_vector)
def perform_VPR(query_info, ref_map_features): 'OUTPUT: Matching Score and Best Matched Image.' vector_2 = query_info[0] regional_goodness = query_info[1] confusion_vector = np.zeros(len(ref_map_features), dtype=np.float32) ref_desc = ref_map_features for ref in range(len(ref_map_features)): score = conv_match_dotproduct(vector_2.astype('float64'), ref_desc[ref].astype('float64'), regional_goodness, total_no_of_regions) confusion_vector[ref] = score return (np.amax(confusion_vector), np.argmax(confusion_vector), confusion_vector)<|docstring|>INPUT: Query desc and reference list of images' features to be matched.<|endoftext|>
20a622012c7dcbf107ce2bf93bc7c4815fa31b92af499ae8fd7dc1b2a3b14043
def check_inputs(self): ' Ensure all layers exist\n ' check_vals = {} good = True for lyr in [self.pop_layer, self.urban_layer, self.urban_hd_layer]: check_vals[lyr] = os.path.exists(lyr) if (not check_vals[lyr]): good = False self.check_vals = check_vals return good
Ensure all layers exist
src/urban_helper.py
check_inputs
eigotateishi/GOST_Urban
3
python
def check_inputs(self): ' \n ' check_vals = {} good = True for lyr in [self.pop_layer, self.urban_layer, self.urban_hd_layer]: check_vals[lyr] = os.path.exists(lyr) if (not check_vals[lyr]): good = False self.check_vals = check_vals return good
def check_inputs(self): ' \n ' check_vals = {} good = True for lyr in [self.pop_layer, self.urban_layer, self.urban_hd_layer]: check_vals[lyr] = os.path.exists(lyr) if (not check_vals[lyr]): good = False self.check_vals = check_vals return good<|docstring|>Ensure all layers exist<|endoftext|>
2fcf653d9492809400f651227f0d857163e020be3f1f0d94cba2e650129039f9
def __init__(self, iso3, output_folder, country_bounds, pop_files, final_folder='', ghspop_suffix=''): " Create object for managing input data for summarizing urban extents\n \n INPUT\n :param: iso3 - string describing iso3 code\n :param: output_folder - string path to folder to hold results\n :param: country_bounds - geopandas dataframe of admin0 boundary\n \n \n NAMING CONVENTION\n To save this renaming step on my side, which can also induce mistakes, would be possible for you Ben to rename the files in your code directly? This would be also helpful for all other countries we have to do, and for the 1km*1km rasters.\n My conventions are pretty simple. All rasters starts with the three lettres of the country and then _ as you do, and then 3 lettres for the variable, and possibly two figures for the year. So for instance for Tanzania, this is:\n tza_ele tza_slo tza_wat for elevation, slope and water\n tza_gpo tza_gbu for GHS population and built-up\n tza_upo15 and tza_upo18 for WorldPop population unconstrained \n tza_cpo15 and tza_cpo18 for WorldPop population constrained.\n Then for 1km*1km raster, names are the same except that the three lettres of the country's name are followed by 1k, ie tza1k_slo, tza1k_ele and so on.\n " self.iso3 = iso3 self.out_folder = output_folder self.suffix = ghspop_suffix if (final_folder == ''): self.final_folder = os.path.join(self.out_folder, 'FINAL_STANDARD') else: self.final_folder = os.path.join(self.out_folder, final_folder) if (not os.path.exists(self.out_folder)): os.makedirs(self.out_folder) if (not os.path.exists(self.final_folder)): os.makedirs(self.final_folder) self.dem_file = os.path.join(output_folder, ('%s_ele.tif' % iso3.lower())) self.slope_file = os.path.join(output_folder, ('%s_slo.tif' % iso3.lower())) self.lc_file = os.path.join(output_folder, ('%s_lc.tif' % iso3.lower())) self.lc_file_h20 = os.path.join(output_folder, ('%s_wat_lc.tif' % iso3.lower())) self.ghsl_h20 = os.path.join(output_folder, ('%s_wat.tif' % iso3.lower())) self.ghspop_file = os.path.join(output_folder, ('%s_gpo.tif' % iso3.lower())) self.ghspop1k_file = os.path.join(output_folder, ('%s1k_gpo.tif' % iso3.lower())) self.ghsbuilt_file = os.path.join(output_folder, ('%s_gbu.tif' % iso3.lower())) self.ghssmod_file = os.path.join(output_folder, ('%s_gsmod.tif' % iso3.lower())) self.admin_file = os.path.join(output_folder, ('%s_adm.tif' % iso3.lower())) self.admin_shp = os.path.join(self.final_folder, ('%s_adm.shp' % iso3.lower())) self.pop_files = [] for fileDef in pop_files: out_pop_file = os.path.join(output_folder, fileDef[1]) self.pop_files.append(out_pop_file) if (not os.path.exists(out_pop_file)): shutil.copy(fileDef[0], out_pop_file) if (ghspop_suffix == '1k'): self.pop_files.append(self.ghspop1k_file) else: self.pop_files.append(self.ghspop_file) shutil.copy(self.ghspop_file, os.path.join(self.final_folder, os.path.basename(self.ghspop_file))) self.inD = country_bounds if (not os.path.exists(self.admin_shp)): self.inD.to_file(self.admin_shp)
Create object for managing input data for summarizing urban extents INPUT :param: iso3 - string describing iso3 code :param: output_folder - string path to folder to hold results :param: country_bounds - geopandas dataframe of admin0 boundary NAMING CONVENTION To save this renaming step on my side, which can also induce mistakes, would be possible for you Ben to rename the files in your code directly? This would be also helpful for all other countries we have to do, and for the 1km*1km rasters. My conventions are pretty simple. All rasters starts with the three lettres of the country and then _ as you do, and then 3 lettres for the variable, and possibly two figures for the year. So for instance for Tanzania, this is: tza_ele tza_slo tza_wat for elevation, slope and water tza_gpo tza_gbu for GHS population and built-up tza_upo15 and tza_upo18 for WorldPop population unconstrained tza_cpo15 and tza_cpo18 for WorldPop population constrained. Then for 1km*1km raster, names are the same except that the three lettres of the country's name are followed by 1k, ie tza1k_slo, tza1k_ele and so on.
src/urban_helper.py
__init__
eigotateishi/GOST_Urban
3
python
def __init__(self, iso3, output_folder, country_bounds, pop_files, final_folder=, ghspop_suffix=): " Create object for managing input data for summarizing urban extents\n \n INPUT\n :param: iso3 - string describing iso3 code\n :param: output_folder - string path to folder to hold results\n :param: country_bounds - geopandas dataframe of admin0 boundary\n \n \n NAMING CONVENTION\n To save this renaming step on my side, which can also induce mistakes, would be possible for you Ben to rename the files in your code directly? This would be also helpful for all other countries we have to do, and for the 1km*1km rasters.\n My conventions are pretty simple. All rasters starts with the three lettres of the country and then _ as you do, and then 3 lettres for the variable, and possibly two figures for the year. So for instance for Tanzania, this is:\n tza_ele tza_slo tza_wat for elevation, slope and water\n tza_gpo tza_gbu for GHS population and built-up\n tza_upo15 and tza_upo18 for WorldPop population unconstrained \n tza_cpo15 and tza_cpo18 for WorldPop population constrained.\n Then for 1km*1km raster, names are the same except that the three lettres of the country's name are followed by 1k, ie tza1k_slo, tza1k_ele and so on.\n " self.iso3 = iso3 self.out_folder = output_folder self.suffix = ghspop_suffix if (final_folder == ): self.final_folder = os.path.join(self.out_folder, 'FINAL_STANDARD') else: self.final_folder = os.path.join(self.out_folder, final_folder) if (not os.path.exists(self.out_folder)): os.makedirs(self.out_folder) if (not os.path.exists(self.final_folder)): os.makedirs(self.final_folder) self.dem_file = os.path.join(output_folder, ('%s_ele.tif' % iso3.lower())) self.slope_file = os.path.join(output_folder, ('%s_slo.tif' % iso3.lower())) self.lc_file = os.path.join(output_folder, ('%s_lc.tif' % iso3.lower())) self.lc_file_h20 = os.path.join(output_folder, ('%s_wat_lc.tif' % iso3.lower())) self.ghsl_h20 = os.path.join(output_folder, ('%s_wat.tif' % iso3.lower())) self.ghspop_file = os.path.join(output_folder, ('%s_gpo.tif' % iso3.lower())) self.ghspop1k_file = os.path.join(output_folder, ('%s1k_gpo.tif' % iso3.lower())) self.ghsbuilt_file = os.path.join(output_folder, ('%s_gbu.tif' % iso3.lower())) self.ghssmod_file = os.path.join(output_folder, ('%s_gsmod.tif' % iso3.lower())) self.admin_file = os.path.join(output_folder, ('%s_adm.tif' % iso3.lower())) self.admin_shp = os.path.join(self.final_folder, ('%s_adm.shp' % iso3.lower())) self.pop_files = [] for fileDef in pop_files: out_pop_file = os.path.join(output_folder, fileDef[1]) self.pop_files.append(out_pop_file) if (not os.path.exists(out_pop_file)): shutil.copy(fileDef[0], out_pop_file) if (ghspop_suffix == '1k'): self.pop_files.append(self.ghspop1k_file) else: self.pop_files.append(self.ghspop_file) shutil.copy(self.ghspop_file, os.path.join(self.final_folder, os.path.basename(self.ghspop_file))) self.inD = country_bounds if (not os.path.exists(self.admin_shp)): self.inD.to_file(self.admin_shp)
def __init__(self, iso3, output_folder, country_bounds, pop_files, final_folder=, ghspop_suffix=): " Create object for managing input data for summarizing urban extents\n \n INPUT\n :param: iso3 - string describing iso3 code\n :param: output_folder - string path to folder to hold results\n :param: country_bounds - geopandas dataframe of admin0 boundary\n \n \n NAMING CONVENTION\n To save this renaming step on my side, which can also induce mistakes, would be possible for you Ben to rename the files in your code directly? This would be also helpful for all other countries we have to do, and for the 1km*1km rasters.\n My conventions are pretty simple. All rasters starts with the three lettres of the country and then _ as you do, and then 3 lettres for the variable, and possibly two figures for the year. So for instance for Tanzania, this is:\n tza_ele tza_slo tza_wat for elevation, slope and water\n tza_gpo tza_gbu for GHS population and built-up\n tza_upo15 and tza_upo18 for WorldPop population unconstrained \n tza_cpo15 and tza_cpo18 for WorldPop population constrained.\n Then for 1km*1km raster, names are the same except that the three lettres of the country's name are followed by 1k, ie tza1k_slo, tza1k_ele and so on.\n " self.iso3 = iso3 self.out_folder = output_folder self.suffix = ghspop_suffix if (final_folder == ): self.final_folder = os.path.join(self.out_folder, 'FINAL_STANDARD') else: self.final_folder = os.path.join(self.out_folder, final_folder) if (not os.path.exists(self.out_folder)): os.makedirs(self.out_folder) if (not os.path.exists(self.final_folder)): os.makedirs(self.final_folder) self.dem_file = os.path.join(output_folder, ('%s_ele.tif' % iso3.lower())) self.slope_file = os.path.join(output_folder, ('%s_slo.tif' % iso3.lower())) self.lc_file = os.path.join(output_folder, ('%s_lc.tif' % iso3.lower())) self.lc_file_h20 = os.path.join(output_folder, ('%s_wat_lc.tif' % iso3.lower())) self.ghsl_h20 = os.path.join(output_folder, ('%s_wat.tif' % iso3.lower())) self.ghspop_file = os.path.join(output_folder, ('%s_gpo.tif' % iso3.lower())) self.ghspop1k_file = os.path.join(output_folder, ('%s1k_gpo.tif' % iso3.lower())) self.ghsbuilt_file = os.path.join(output_folder, ('%s_gbu.tif' % iso3.lower())) self.ghssmod_file = os.path.join(output_folder, ('%s_gsmod.tif' % iso3.lower())) self.admin_file = os.path.join(output_folder, ('%s_adm.tif' % iso3.lower())) self.admin_shp = os.path.join(self.final_folder, ('%s_adm.shp' % iso3.lower())) self.pop_files = [] for fileDef in pop_files: out_pop_file = os.path.join(output_folder, fileDef[1]) self.pop_files.append(out_pop_file) if (not os.path.exists(out_pop_file)): shutil.copy(fileDef[0], out_pop_file) if (ghspop_suffix == '1k'): self.pop_files.append(self.ghspop1k_file) else: self.pop_files.append(self.ghspop_file) shutil.copy(self.ghspop_file, os.path.join(self.final_folder, os.path.basename(self.ghspop_file))) self.inD = country_bounds if (not os.path.exists(self.admin_shp)): self.inD.to_file(self.admin_shp)<|docstring|>Create object for managing input data for summarizing urban extents INPUT :param: iso3 - string describing iso3 code :param: output_folder - string path to folder to hold results :param: country_bounds - geopandas dataframe of admin0 boundary NAMING CONVENTION To save this renaming step on my side, which can also induce mistakes, would be possible for you Ben to rename the files in your code directly? This would be also helpful for all other countries we have to do, and for the 1km*1km rasters. My conventions are pretty simple. All rasters starts with the three lettres of the country and then _ as you do, and then 3 lettres for the variable, and possibly two figures for the year. So for instance for Tanzania, this is: tza_ele tza_slo tza_wat for elevation, slope and water tza_gpo tza_gbu for GHS population and built-up tza_upo15 and tza_upo18 for WorldPop population unconstrained tza_cpo15 and tza_cpo18 for WorldPop population constrained. Then for 1km*1km raster, names are the same except that the three lettres of the country's name are followed by 1k, ie tza1k_slo, tza1k_ele and so on.<|endoftext|>
80e1ed231e44a6a5902ab848f128a57b70289c4c28b0a4cf7caec27d2fede328
def process_dem(self, global_dem=''): ' Download DEM from AWS, calculate slope\n ' if ((not os.path.exists(self.dem_file)) and (global_dem == '')): tPrint('Downloading DEM') elevation.clip(bounds=self.inD.total_bounds, max_download_tiles=90000, output=self.dem_file, product='SRTM3') if ((not os.path.exists(self.dem_file)) and (not (global_dem == ''))): tPrint('Downloading DEM') rMisc.clipRaster(rasterio.open(global_dem), self.inD, self.dem_file) if ((not os.path.exists(self.slope_file)) and os.path.exists(self.dem_file)): tPrint('Calculating slope') in_dem = rasterio.open(self.dem_file) in_dem_data = in_dem.read() beau = richdem.rdarray(in_dem_data[(0, :, :)], no_data=in_dem.meta['nodata']) slope = richdem.TerrainAttribute(beau, attrib='slope_riserun') meta = in_dem.meta.copy() meta.update(dtype=slope.dtype) with rasterio.open(self.slope_file, 'w', **meta) as outR: outR.write_band(1, slope)
Download DEM from AWS, calculate slope
src/urban_helper.py
process_dem
eigotateishi/GOST_Urban
3
python
def process_dem(self, global_dem=): ' \n ' if ((not os.path.exists(self.dem_file)) and (global_dem == )): tPrint('Downloading DEM') elevation.clip(bounds=self.inD.total_bounds, max_download_tiles=90000, output=self.dem_file, product='SRTM3') if ((not os.path.exists(self.dem_file)) and (not (global_dem == ))): tPrint('Downloading DEM') rMisc.clipRaster(rasterio.open(global_dem), self.inD, self.dem_file) if ((not os.path.exists(self.slope_file)) and os.path.exists(self.dem_file)): tPrint('Calculating slope') in_dem = rasterio.open(self.dem_file) in_dem_data = in_dem.read() beau = richdem.rdarray(in_dem_data[(0, :, :)], no_data=in_dem.meta['nodata']) slope = richdem.TerrainAttribute(beau, attrib='slope_riserun') meta = in_dem.meta.copy() meta.update(dtype=slope.dtype) with rasterio.open(self.slope_file, 'w', **meta) as outR: outR.write_band(1, slope)
def process_dem(self, global_dem=): ' \n ' if ((not os.path.exists(self.dem_file)) and (global_dem == )): tPrint('Downloading DEM') elevation.clip(bounds=self.inD.total_bounds, max_download_tiles=90000, output=self.dem_file, product='SRTM3') if ((not os.path.exists(self.dem_file)) and (not (global_dem == ))): tPrint('Downloading DEM') rMisc.clipRaster(rasterio.open(global_dem), self.inD, self.dem_file) if ((not os.path.exists(self.slope_file)) and os.path.exists(self.dem_file)): tPrint('Calculating slope') in_dem = rasterio.open(self.dem_file) in_dem_data = in_dem.read() beau = richdem.rdarray(in_dem_data[(0, :, :)], no_data=in_dem.meta['nodata']) slope = richdem.TerrainAttribute(beau, attrib='slope_riserun') meta = in_dem.meta.copy() meta.update(dtype=slope.dtype) with rasterio.open(self.slope_file, 'w', **meta) as outR: outR.write_band(1, slope)<|docstring|>Download DEM from AWS, calculate slope<|endoftext|>
fbfd796611a2954963f8905eb906f2b7a2e911b2dfbbcc163bea4aaf596c7550
def extract_layers(self, global_landcover, global_ghspop, global_ghspop1k, global_ghbuilt, global_ghsl, global_smod): ' extract global layers for current country\n ' if (not os.path.exists(self.lc_file_h20)): tPrint('Extracting water') if (not os.path.exists(self.lc_file)): rMisc.clipRaster(rasterio.open(global_landcover), self.inD, self.lc_file) in_lc = rasterio.open(self.lc_file) inL = in_lc.read() lcmeta = in_lc.meta.copy() tempL = (inL == 210).astype(lcmeta['dtype']) lcmeta.update(nodata=255) with rasterio.open(self.lc_file_h20, 'w', **lcmeta) as out: out.write(tempL) os.remove(self.lc_file) if (not os.path.exists(self.ghsl_h20)): tPrint('Extracting water from GHSL') inR = rasterio.open(global_ghsl) ul = inR.index(*self.inD.total_bounds[0:2]) lr = inR.index(*self.inD.total_bounds[2:4]) window = ((float(lr[0]), float((ul[0] + 1))), (float(ul[1]), float((lr[1] + 1)))) data = inR.read(1, window=window, masked=False) data = (data == 1) b = self.inD.total_bounds new_transform = rasterio.transform.from_bounds(b[0], b[1], b[2], b[3], data.shape[1], data.shape[0]) meta = inR.meta.copy() meta.update(driver='GTiff', width=data.shape[1], height=data.shape[0], transform=new_transform) data = data.astype(meta['dtype']) with rasterio.open(self.ghsl_h20, 'w', **meta) as outR: outR.write_band(1, data) '\n bounds = box(*self.inD.total_bounds)\n if inG.crs != self.inD.crs:\n destCRS = pyproj.Proj(inG.crs)\n fromCRS = pyproj.Proj(self.inD.crs)\n projector = partial(pyproj.transform, fromCRS, destCRS)\n bounds = transform(projector, bounds)\n def getFeatures(gdf):\n #Function to parse features from GeoDataFrame in such a manner that rasterio wants them\n return [json.loads(gdf.to_json())[\'features\'][0][\'geometry\']]\n tD = gpd.GeoDataFrame([[1]], geometry=[bounds])\n coords = getFeatures(tD)\n out_img, out_transform = mask(inG, shapes=coords, crop=True)\n out_meta = inG.meta.copy()\n out_meta.update({"driver": "GTiff",\n "height": out_img.shape[1],\n "width": out_img.shape[2],\n "transform": out_transform})\n water_data = (out_img == 1).astype(out_meta[\'dtype\'])\n with rasterio.open(self.ghsl_h20, \'w\', **out_meta) as outR:\n outR.write(water_data)\n ' if (not os.path.exists(self.ghspop_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop), self.inD, self.ghspop_file) if (not os.path.exists(self.ghspop1k_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop1k), self.inD, self.ghspop1k_file) if (not os.path.exists(self.ghsbuilt_file)): tPrint('Clipping GHS-Built') rMisc.clipRaster(rasterio.open(global_ghbuilt), self.inD, self.ghsbuilt_file) if (not os.path.exists(self.ghssmod_file)): tPrint('Clipping GHS-SMOD') rMisc.clipRaster(rasterio.open(global_smod), self.inD, self.ghssmod_file) if (not os.path.exists(self.admin_file)): tPrint('Rasterizing admin boundaries') xx = rasterio.open(self.ghspop_file) res = xx.meta['transform'][0] tempD = self.inD.to_crs(xx.crs) shapes = ((row['geometry'], 1) for (idx, row) in tempD.iterrows()) burned = features.rasterize(shapes=shapes, out_shape=xx.shape, fill=0, transform=xx.meta['transform'], dtype='int16') meta = xx.meta.copy() meta.update(dtype=burned.dtype) with rasterio.open(self.admin_file, 'w', **meta) as outR: outR.write_band(1, burned)
extract global layers for current country
src/urban_helper.py
extract_layers
eigotateishi/GOST_Urban
3
python
def extract_layers(self, global_landcover, global_ghspop, global_ghspop1k, global_ghbuilt, global_ghsl, global_smod): ' \n ' if (not os.path.exists(self.lc_file_h20)): tPrint('Extracting water') if (not os.path.exists(self.lc_file)): rMisc.clipRaster(rasterio.open(global_landcover), self.inD, self.lc_file) in_lc = rasterio.open(self.lc_file) inL = in_lc.read() lcmeta = in_lc.meta.copy() tempL = (inL == 210).astype(lcmeta['dtype']) lcmeta.update(nodata=255) with rasterio.open(self.lc_file_h20, 'w', **lcmeta) as out: out.write(tempL) os.remove(self.lc_file) if (not os.path.exists(self.ghsl_h20)): tPrint('Extracting water from GHSL') inR = rasterio.open(global_ghsl) ul = inR.index(*self.inD.total_bounds[0:2]) lr = inR.index(*self.inD.total_bounds[2:4]) window = ((float(lr[0]), float((ul[0] + 1))), (float(ul[1]), float((lr[1] + 1)))) data = inR.read(1, window=window, masked=False) data = (data == 1) b = self.inD.total_bounds new_transform = rasterio.transform.from_bounds(b[0], b[1], b[2], b[3], data.shape[1], data.shape[0]) meta = inR.meta.copy() meta.update(driver='GTiff', width=data.shape[1], height=data.shape[0], transform=new_transform) data = data.astype(meta['dtype']) with rasterio.open(self.ghsl_h20, 'w', **meta) as outR: outR.write_band(1, data) '\n bounds = box(*self.inD.total_bounds)\n if inG.crs != self.inD.crs:\n destCRS = pyproj.Proj(inG.crs)\n fromCRS = pyproj.Proj(self.inD.crs)\n projector = partial(pyproj.transform, fromCRS, destCRS)\n bounds = transform(projector, bounds)\n def getFeatures(gdf):\n #Function to parse features from GeoDataFrame in such a manner that rasterio wants them\n return [json.loads(gdf.to_json())[\'features\'][0][\'geometry\']]\n tD = gpd.GeoDataFrame([[1]], geometry=[bounds])\n coords = getFeatures(tD)\n out_img, out_transform = mask(inG, shapes=coords, crop=True)\n out_meta = inG.meta.copy()\n out_meta.update({"driver": "GTiff",\n "height": out_img.shape[1],\n "width": out_img.shape[2],\n "transform": out_transform})\n water_data = (out_img == 1).astype(out_meta[\'dtype\'])\n with rasterio.open(self.ghsl_h20, \'w\', **out_meta) as outR:\n outR.write(water_data)\n ' if (not os.path.exists(self.ghspop_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop), self.inD, self.ghspop_file) if (not os.path.exists(self.ghspop1k_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop1k), self.inD, self.ghspop1k_file) if (not os.path.exists(self.ghsbuilt_file)): tPrint('Clipping GHS-Built') rMisc.clipRaster(rasterio.open(global_ghbuilt), self.inD, self.ghsbuilt_file) if (not os.path.exists(self.ghssmod_file)): tPrint('Clipping GHS-SMOD') rMisc.clipRaster(rasterio.open(global_smod), self.inD, self.ghssmod_file) if (not os.path.exists(self.admin_file)): tPrint('Rasterizing admin boundaries') xx = rasterio.open(self.ghspop_file) res = xx.meta['transform'][0] tempD = self.inD.to_crs(xx.crs) shapes = ((row['geometry'], 1) for (idx, row) in tempD.iterrows()) burned = features.rasterize(shapes=shapes, out_shape=xx.shape, fill=0, transform=xx.meta['transform'], dtype='int16') meta = xx.meta.copy() meta.update(dtype=burned.dtype) with rasterio.open(self.admin_file, 'w', **meta) as outR: outR.write_band(1, burned)
def extract_layers(self, global_landcover, global_ghspop, global_ghspop1k, global_ghbuilt, global_ghsl, global_smod): ' \n ' if (not os.path.exists(self.lc_file_h20)): tPrint('Extracting water') if (not os.path.exists(self.lc_file)): rMisc.clipRaster(rasterio.open(global_landcover), self.inD, self.lc_file) in_lc = rasterio.open(self.lc_file) inL = in_lc.read() lcmeta = in_lc.meta.copy() tempL = (inL == 210).astype(lcmeta['dtype']) lcmeta.update(nodata=255) with rasterio.open(self.lc_file_h20, 'w', **lcmeta) as out: out.write(tempL) os.remove(self.lc_file) if (not os.path.exists(self.ghsl_h20)): tPrint('Extracting water from GHSL') inR = rasterio.open(global_ghsl) ul = inR.index(*self.inD.total_bounds[0:2]) lr = inR.index(*self.inD.total_bounds[2:4]) window = ((float(lr[0]), float((ul[0] + 1))), (float(ul[1]), float((lr[1] + 1)))) data = inR.read(1, window=window, masked=False) data = (data == 1) b = self.inD.total_bounds new_transform = rasterio.transform.from_bounds(b[0], b[1], b[2], b[3], data.shape[1], data.shape[0]) meta = inR.meta.copy() meta.update(driver='GTiff', width=data.shape[1], height=data.shape[0], transform=new_transform) data = data.astype(meta['dtype']) with rasterio.open(self.ghsl_h20, 'w', **meta) as outR: outR.write_band(1, data) '\n bounds = box(*self.inD.total_bounds)\n if inG.crs != self.inD.crs:\n destCRS = pyproj.Proj(inG.crs)\n fromCRS = pyproj.Proj(self.inD.crs)\n projector = partial(pyproj.transform, fromCRS, destCRS)\n bounds = transform(projector, bounds)\n def getFeatures(gdf):\n #Function to parse features from GeoDataFrame in such a manner that rasterio wants them\n return [json.loads(gdf.to_json())[\'features\'][0][\'geometry\']]\n tD = gpd.GeoDataFrame([[1]], geometry=[bounds])\n coords = getFeatures(tD)\n out_img, out_transform = mask(inG, shapes=coords, crop=True)\n out_meta = inG.meta.copy()\n out_meta.update({"driver": "GTiff",\n "height": out_img.shape[1],\n "width": out_img.shape[2],\n "transform": out_transform})\n water_data = (out_img == 1).astype(out_meta[\'dtype\'])\n with rasterio.open(self.ghsl_h20, \'w\', **out_meta) as outR:\n outR.write(water_data)\n ' if (not os.path.exists(self.ghspop_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop), self.inD, self.ghspop_file) if (not os.path.exists(self.ghspop1k_file)): tPrint('Extracting GHS-POP') rMisc.clipRaster(rasterio.open(global_ghspop1k), self.inD, self.ghspop1k_file) if (not os.path.exists(self.ghsbuilt_file)): tPrint('Clipping GHS-Built') rMisc.clipRaster(rasterio.open(global_ghbuilt), self.inD, self.ghsbuilt_file) if (not os.path.exists(self.ghssmod_file)): tPrint('Clipping GHS-SMOD') rMisc.clipRaster(rasterio.open(global_smod), self.inD, self.ghssmod_file) if (not os.path.exists(self.admin_file)): tPrint('Rasterizing admin boundaries') xx = rasterio.open(self.ghspop_file) res = xx.meta['transform'][0] tempD = self.inD.to_crs(xx.crs) shapes = ((row['geometry'], 1) for (idx, row) in tempD.iterrows()) burned = features.rasterize(shapes=shapes, out_shape=xx.shape, fill=0, transform=xx.meta['transform'], dtype='int16') meta = xx.meta.copy() meta.update(dtype=burned.dtype) with rasterio.open(self.admin_file, 'w', **meta) as outR: outR.write_band(1, burned)<|docstring|>extract global layers for current country<|endoftext|>
84518453bc30544fbdf99365594a82e8ec4f77567789f65f74713a74475c6bda
def calculate_urban(self, urb_val=300, hd_urb_val=1500): ' Calculate urban and HD urban extents from population files\n ' ghs_R = rasterio.open(self.ghspop_file) for p_file in self.pop_files: final_pop = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in final_pop): final_pop = final_pop.replace('1k1k', '1k') final_urban = final_pop.replace('.tif', '_urban.tif') final_urban_hd = final_pop.replace('.tif', '_urban_hd.tif') urbanR = urban.urbanGriddedPop(final_pop) in_raster = rasterio.open(final_pop) total_ratio = ((in_raster.res[0] * in_raster.res[1]) / 1000000) print(final_pop) if (not os.path.exists(final_urban)): urban_shp = urbanR.calculateUrban(densVal=(urb_val * total_ratio), totalPopThresh=5000, raster=final_urban) if (not os.path.exists(final_urban_hd)): cluster_shp = urbanR.calculateUrban(densVal=(hd_urb_val * total_ratio), totalPopThresh=50000, raster=final_urban_hd, smooth=True, queen=True)
Calculate urban and HD urban extents from population files
src/urban_helper.py
calculate_urban
eigotateishi/GOST_Urban
3
python
def calculate_urban(self, urb_val=300, hd_urb_val=1500): ' \n ' ghs_R = rasterio.open(self.ghspop_file) for p_file in self.pop_files: final_pop = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in final_pop): final_pop = final_pop.replace('1k1k', '1k') final_urban = final_pop.replace('.tif', '_urban.tif') final_urban_hd = final_pop.replace('.tif', '_urban_hd.tif') urbanR = urban.urbanGriddedPop(final_pop) in_raster = rasterio.open(final_pop) total_ratio = ((in_raster.res[0] * in_raster.res[1]) / 1000000) print(final_pop) if (not os.path.exists(final_urban)): urban_shp = urbanR.calculateUrban(densVal=(urb_val * total_ratio), totalPopThresh=5000, raster=final_urban) if (not os.path.exists(final_urban_hd)): cluster_shp = urbanR.calculateUrban(densVal=(hd_urb_val * total_ratio), totalPopThresh=50000, raster=final_urban_hd, smooth=True, queen=True)
def calculate_urban(self, urb_val=300, hd_urb_val=1500): ' \n ' ghs_R = rasterio.open(self.ghspop_file) for p_file in self.pop_files: final_pop = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in final_pop): final_pop = final_pop.replace('1k1k', '1k') final_urban = final_pop.replace('.tif', '_urban.tif') final_urban_hd = final_pop.replace('.tif', '_urban_hd.tif') urbanR = urban.urbanGriddedPop(final_pop) in_raster = rasterio.open(final_pop) total_ratio = ((in_raster.res[0] * in_raster.res[1]) / 1000000) print(final_pop) if (not os.path.exists(final_urban)): urban_shp = urbanR.calculateUrban(densVal=(urb_val * total_ratio), totalPopThresh=5000, raster=final_urban) if (not os.path.exists(final_urban_hd)): cluster_shp = urbanR.calculateUrban(densVal=(hd_urb_val * total_ratio), totalPopThresh=50000, raster=final_urban_hd, smooth=True, queen=True)<|docstring|>Calculate urban and HD urban extents from population files<|endoftext|>
e3b607cea159e8e452880231858dbda32e26642fa3980ad170ea78e4324f6251
def pop_zonal_admin(self, admin_layer): ' calculate urban and rural \n \n :param: - admin_layer\n ' for p_file in self.pop_files: pop_file = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in pop_file): pop_file = pop_file.replace('1k1k', '1k') yy = summarize_population(pop_file, admin_layer) if yy.check_inputs(): res = yy.calculate_zonal(out_name='') try: final = final.join(res) except: final = res else: print(('Error summarizing population for %s' % pop_file)) admin_layer = admin_layer.reset_index() final = final.filter(regex='_SUM') final = final.join(admin_layer) final = final.drop(['geometry'], axis=1) return final
calculate urban and rural :param: - admin_layer
src/urban_helper.py
pop_zonal_admin
eigotateishi/GOST_Urban
3
python
def pop_zonal_admin(self, admin_layer): ' calculate urban and rural \n \n :param: - admin_layer\n ' for p_file in self.pop_files: pop_file = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in pop_file): pop_file = pop_file.replace('1k1k', '1k') yy = summarize_population(pop_file, admin_layer) if yy.check_inputs(): res = yy.calculate_zonal(out_name=) try: final = final.join(res) except: final = res else: print(('Error summarizing population for %s' % pop_file)) admin_layer = admin_layer.reset_index() final = final.filter(regex='_SUM') final = final.join(admin_layer) final = final.drop(['geometry'], axis=1) return final
def pop_zonal_admin(self, admin_layer): ' calculate urban and rural \n \n :param: - admin_layer\n ' for p_file in self.pop_files: pop_file = os.path.join(self.final_folder, os.path.basename(p_file).replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix)))) if ('1k1k' in pop_file): pop_file = pop_file.replace('1k1k', '1k') yy = summarize_population(pop_file, admin_layer) if yy.check_inputs(): res = yy.calculate_zonal(out_name=) try: final = final.join(res) except: final = res else: print(('Error summarizing population for %s' % pop_file)) admin_layer = admin_layer.reset_index() final = final.filter(regex='_SUM') final = final.join(admin_layer) final = final.drop(['geometry'], axis=1) return final<|docstring|>calculate urban and rural :param: - admin_layer<|endoftext|>
2cebd5bcea87b5dcbc55b8ebdcb355030d915f0f8ab5fdd3477cdccdacfedec7
def compare_pop_rasters(self, verbose=True): ' read in and summarize population rasters \n ' all_res = [] for pFile in self.pop_files: inR = rasterio.open(pFile) inD = inR.read() inD = inD[(inD > 0)] all_res.append([os.path.basename(pFile), inD.sum()]) if verbose: print(f'{os.path.basename(pFile)}: {inD.sum()}') return all_res
read in and summarize population rasters
src/urban_helper.py
compare_pop_rasters
eigotateishi/GOST_Urban
3
python
def compare_pop_rasters(self, verbose=True): ' \n ' all_res = [] for pFile in self.pop_files: inR = rasterio.open(pFile) inD = inR.read() inD = inD[(inD > 0)] all_res.append([os.path.basename(pFile), inD.sum()]) if verbose: print(f'{os.path.basename(pFile)}: {inD.sum()}') return all_res
def compare_pop_rasters(self, verbose=True): ' \n ' all_res = [] for pFile in self.pop_files: inR = rasterio.open(pFile) inD = inR.read() inD = inD[(inD > 0)] all_res.append([os.path.basename(pFile), inD.sum()]) if verbose: print(f'{os.path.basename(pFile)}: {inD.sum()}') return all_res<|docstring|>read in and summarize population rasters<|endoftext|>
14b396ad7cceb3faaf6e2b94ae11d3d83a5ec5ecd8343dbf51585c82345fcfb0
def evaluateOutput(self, admin_stats, commune_stats): '\n Check the outputs to determine if processing worked correctly\n \n 1. compare population totals between raw, 250m and 1km data\n 2. Calculate urbanization rate\n 3. Water mask\n a. calculate overlap between water classes\n b. calculate overlap between water and population\n c. calculate overlap between water and urban\n \n https://ghsl.jrc.ec.europa.eu/documents/cfs01/V3/CFS_Ghana.pdf\n ' stats_file = os.path.join(self.out_folder, ('DATA_EVALUATION_%s_%s.txt' % (self.iso3, self.suffix))) with open(stats_file, 'w') as out_stats: pop_comparison = self.compare_pop_rasters(verbose=False) out_stats.write('***** Evaluate Total Population *****\n') for x in pop_comparison: out_stats.write(f'''{x[0]}: {x[1]} ''') pop_file_defs = [] for pop_file in self.pop_files: name = 'GHS' if ('upo' in pop_file): name = ('WP_U_%s' % pop_file[(- 6):(- 4)]) if ('cpo' in pop_file): name = ('WP_C_%s' % pop_file[(- 6):(- 4)]) pop_file_base = os.path.basename(pop_file) if (self.suffix == '1k'): pop_file_base = pop_file_base.replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix))) if ('1k1k' in pop_file_base): pop_file_base = pop_file_base.replace('1k1k', '1k') out_pop_file = os.path.join(self.final_folder, pop_file_base) urban_pop_file = out_pop_file.replace('.tif', '_urban.tif') hd_pop_file = out_pop_file.replace('.tif', '_urban_hd.tif') pop_file_defs.append([out_pop_file, urban_pop_file, hd_pop_file, name]) out_stats.write('***** Evaluate Urbanization *****\n') for fileDef in pop_file_defs: pFile = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] try: inPop = rasterio.open(pFile).read() inPop = (inPop * (inPop > 0)) inUrb = rasterio.open(urb_file).read() inHd = rasterio.open(hd_file).read() tPop = inPop.sum() urbPop = (inPop * inUrb).sum() hdPop = (inPop * inHd).sum() out_stats.write(f'''{name}: TotalPop: {tPop.round(0)}, UrbanPop: {urbPop.round(0)}, HD Pop: {hdPop.round(0)} ''') out_stats.write(f'''{name}: {((urbPop / tPop) * 100).round(2)}% Urban; {((hdPop / tPop) * 100).round(2)}% HD Urban ''') except: print(f'Error processing {name}') print(fileDef) out_stats.write('***** Evaluate SMOD ******\n') smod_vals = [10, 11, 12, 13, 21, 22, 23, 30] inSMOD = rasterio.open(os.path.join(self.final_folder, os.path.basename(self.ghssmod_file).replace(('%s' % self.iso3.lower()), ('%s%s' % (self.iso3.lower(), self.suffix))))) smod = inSMOD.read() for pFile in self.pop_files: if ('gpo' in pFile): inPop = rasterio.open(pFile) pop = inPop.read() pop[(pop < 0)] = 0 total_pop = pop.sum() total_per = 0 for val in smod_vals: cur_smod = (smod == val).astype(int) cur_pop = (pop * cur_smod) total_curpop = cur_pop.sum() perUrban = ((total_curpop.sum() / total_pop) * 100) if (val > 20): total_per = (total_per + perUrban) out_stats.write(f'''{val}: {perUrban} ''') out_stats.write(f'''Total Urban: {total_per} ''') '3. Water mask \n ' out_stats.write('***** Evaluate Water Intersection *****\n') water_ghsl = os.path.join(self.final_folder, ('%s%s_wat.tif' % (self.iso3.lower(), self.suffix))) water_lc = os.path.join(self.final_folder, ('%s%s_wat_lc.tif' % (self.iso3.lower(), self.suffix))) inWG = rasterio.open(water_ghsl) wgData = inWG.read() wgData[(wgData == inWG.meta['nodata'])] = 0 inWLC = rasterio.open(water_lc) wlcData = inWLC.read() wlcData[(wlcData == inWLC.meta['nodata'])] = 0 combo = (wgData + wlcData) out_stats.write(f'''WATER: GHSL count: {wgData.sum()}; LC count: {wlcData.sum()}; overlap: {(combo == 2).sum()} ''') out_stats.write('***** Evaluate Water Population Overlap *****\n') for fileDef in pop_file_defs: pop_file = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] cur_pop = rasterio.open(pop_file) curP = cur_pop.read() curP[(curP == cur_pop.meta['nodata'])] = 0 urb = rasterio.open(urb_file).read() hd = rasterio.open(hd_file).read() out_stats.write(f'''WATER {name} Population: TotalPop: {curP.sum().round()}, WaterPop GHSL: {(curP * wgData).sum().round()}, WaterPop LC: {(curP * wlcData).sum().round()} ''') out_stats.write(f'''WATER {name} Urban Cells: TotalUrban Cells: {urb.sum().round()}, WaterUrban GHSL: {(urb * wgData).sum()}, WaterUrb LC: {(urb * wlcData).sum()} ''') out_stats.write(f'''WATER {name} HD Cells: TotalPop: {hd.sum().round()}, WaterHD GHSL: {(hd * wgData).sum()}, WaterHD LC: {(hd * wlcData).sum()} ''') for sFile in [admin_stats, commune_stats]: if os.path.exists(sFile): tPrint(sFile) file_name = os.path.basename(sFile) inD = pd.read_csv(sFile, index_col=0) out_stats.write(f'''***** Summarizing {file_name} ''') bad_cols = ['index', 'OBJECTID', 'WB_ADM1_CO', 'WB_ADM0_CO', 'WB_ADM2_CO', 'Shape_Leng', 'Shape_Area'] for col in inD.columns: if (not (col in bad_cols)): curD = inD[col] try: curD_sum = curD.loc[(curD > 0)].sum() out_stats.write(f'''{col}: {round(curD_sum)} ''') except: pass
Check the outputs to determine if processing worked correctly 1. compare population totals between raw, 250m and 1km data 2. Calculate urbanization rate 3. Water mask a. calculate overlap between water classes b. calculate overlap between water and population c. calculate overlap between water and urban https://ghsl.jrc.ec.europa.eu/documents/cfs01/V3/CFS_Ghana.pdf
src/urban_helper.py
evaluateOutput
eigotateishi/GOST_Urban
3
python
def evaluateOutput(self, admin_stats, commune_stats): '\n Check the outputs to determine if processing worked correctly\n \n 1. compare population totals between raw, 250m and 1km data\n 2. Calculate urbanization rate\n 3. Water mask\n a. calculate overlap between water classes\n b. calculate overlap between water and population\n c. calculate overlap between water and urban\n \n https://ghsl.jrc.ec.europa.eu/documents/cfs01/V3/CFS_Ghana.pdf\n ' stats_file = os.path.join(self.out_folder, ('DATA_EVALUATION_%s_%s.txt' % (self.iso3, self.suffix))) with open(stats_file, 'w') as out_stats: pop_comparison = self.compare_pop_rasters(verbose=False) out_stats.write('***** Evaluate Total Population *****\n') for x in pop_comparison: out_stats.write(f'{x[0]}: {x[1]} ') pop_file_defs = [] for pop_file in self.pop_files: name = 'GHS' if ('upo' in pop_file): name = ('WP_U_%s' % pop_file[(- 6):(- 4)]) if ('cpo' in pop_file): name = ('WP_C_%s' % pop_file[(- 6):(- 4)]) pop_file_base = os.path.basename(pop_file) if (self.suffix == '1k'): pop_file_base = pop_file_base.replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix))) if ('1k1k' in pop_file_base): pop_file_base = pop_file_base.replace('1k1k', '1k') out_pop_file = os.path.join(self.final_folder, pop_file_base) urban_pop_file = out_pop_file.replace('.tif', '_urban.tif') hd_pop_file = out_pop_file.replace('.tif', '_urban_hd.tif') pop_file_defs.append([out_pop_file, urban_pop_file, hd_pop_file, name]) out_stats.write('***** Evaluate Urbanization *****\n') for fileDef in pop_file_defs: pFile = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] try: inPop = rasterio.open(pFile).read() inPop = (inPop * (inPop > 0)) inUrb = rasterio.open(urb_file).read() inHd = rasterio.open(hd_file).read() tPop = inPop.sum() urbPop = (inPop * inUrb).sum() hdPop = (inPop * inHd).sum() out_stats.write(f'{name}: TotalPop: {tPop.round(0)}, UrbanPop: {urbPop.round(0)}, HD Pop: {hdPop.round(0)} ') out_stats.write(f'{name}: {((urbPop / tPop) * 100).round(2)}% Urban; {((hdPop / tPop) * 100).round(2)}% HD Urban ') except: print(f'Error processing {name}') print(fileDef) out_stats.write('***** Evaluate SMOD ******\n') smod_vals = [10, 11, 12, 13, 21, 22, 23, 30] inSMOD = rasterio.open(os.path.join(self.final_folder, os.path.basename(self.ghssmod_file).replace(('%s' % self.iso3.lower()), ('%s%s' % (self.iso3.lower(), self.suffix))))) smod = inSMOD.read() for pFile in self.pop_files: if ('gpo' in pFile): inPop = rasterio.open(pFile) pop = inPop.read() pop[(pop < 0)] = 0 total_pop = pop.sum() total_per = 0 for val in smod_vals: cur_smod = (smod == val).astype(int) cur_pop = (pop * cur_smod) total_curpop = cur_pop.sum() perUrban = ((total_curpop.sum() / total_pop) * 100) if (val > 20): total_per = (total_per + perUrban) out_stats.write(f'{val}: {perUrban} ') out_stats.write(f'Total Urban: {total_per} ') '3. Water mask \n ' out_stats.write('***** Evaluate Water Intersection *****\n') water_ghsl = os.path.join(self.final_folder, ('%s%s_wat.tif' % (self.iso3.lower(), self.suffix))) water_lc = os.path.join(self.final_folder, ('%s%s_wat_lc.tif' % (self.iso3.lower(), self.suffix))) inWG = rasterio.open(water_ghsl) wgData = inWG.read() wgData[(wgData == inWG.meta['nodata'])] = 0 inWLC = rasterio.open(water_lc) wlcData = inWLC.read() wlcData[(wlcData == inWLC.meta['nodata'])] = 0 combo = (wgData + wlcData) out_stats.write(f'WATER: GHSL count: {wgData.sum()}; LC count: {wlcData.sum()}; overlap: {(combo == 2).sum()} ') out_stats.write('***** Evaluate Water Population Overlap *****\n') for fileDef in pop_file_defs: pop_file = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] cur_pop = rasterio.open(pop_file) curP = cur_pop.read() curP[(curP == cur_pop.meta['nodata'])] = 0 urb = rasterio.open(urb_file).read() hd = rasterio.open(hd_file).read() out_stats.write(f'WATER {name} Population: TotalPop: {curP.sum().round()}, WaterPop GHSL: {(curP * wgData).sum().round()}, WaterPop LC: {(curP * wlcData).sum().round()} ') out_stats.write(f'WATER {name} Urban Cells: TotalUrban Cells: {urb.sum().round()}, WaterUrban GHSL: {(urb * wgData).sum()}, WaterUrb LC: {(urb * wlcData).sum()} ') out_stats.write(f'WATER {name} HD Cells: TotalPop: {hd.sum().round()}, WaterHD GHSL: {(hd * wgData).sum()}, WaterHD LC: {(hd * wlcData).sum()} ') for sFile in [admin_stats, commune_stats]: if os.path.exists(sFile): tPrint(sFile) file_name = os.path.basename(sFile) inD = pd.read_csv(sFile, index_col=0) out_stats.write(f'***** Summarizing {file_name} ') bad_cols = ['index', 'OBJECTID', 'WB_ADM1_CO', 'WB_ADM0_CO', 'WB_ADM2_CO', 'Shape_Leng', 'Shape_Area'] for col in inD.columns: if (not (col in bad_cols)): curD = inD[col] try: curD_sum = curD.loc[(curD > 0)].sum() out_stats.write(f'{col}: {round(curD_sum)} ') except: pass
def evaluateOutput(self, admin_stats, commune_stats): '\n Check the outputs to determine if processing worked correctly\n \n 1. compare population totals between raw, 250m and 1km data\n 2. Calculate urbanization rate\n 3. Water mask\n a. calculate overlap between water classes\n b. calculate overlap between water and population\n c. calculate overlap between water and urban\n \n https://ghsl.jrc.ec.europa.eu/documents/cfs01/V3/CFS_Ghana.pdf\n ' stats_file = os.path.join(self.out_folder, ('DATA_EVALUATION_%s_%s.txt' % (self.iso3, self.suffix))) with open(stats_file, 'w') as out_stats: pop_comparison = self.compare_pop_rasters(verbose=False) out_stats.write('***** Evaluate Total Population *****\n') for x in pop_comparison: out_stats.write(f'{x[0]}: {x[1]} ') pop_file_defs = [] for pop_file in self.pop_files: name = 'GHS' if ('upo' in pop_file): name = ('WP_U_%s' % pop_file[(- 6):(- 4)]) if ('cpo' in pop_file): name = ('WP_C_%s' % pop_file[(- 6):(- 4)]) pop_file_base = os.path.basename(pop_file) if (self.suffix == '1k'): pop_file_base = pop_file_base.replace(self.iso3.lower(), ('%s%s' % (self.iso3.lower(), self.suffix))) if ('1k1k' in pop_file_base): pop_file_base = pop_file_base.replace('1k1k', '1k') out_pop_file = os.path.join(self.final_folder, pop_file_base) urban_pop_file = out_pop_file.replace('.tif', '_urban.tif') hd_pop_file = out_pop_file.replace('.tif', '_urban_hd.tif') pop_file_defs.append([out_pop_file, urban_pop_file, hd_pop_file, name]) out_stats.write('***** Evaluate Urbanization *****\n') for fileDef in pop_file_defs: pFile = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] try: inPop = rasterio.open(pFile).read() inPop = (inPop * (inPop > 0)) inUrb = rasterio.open(urb_file).read() inHd = rasterio.open(hd_file).read() tPop = inPop.sum() urbPop = (inPop * inUrb).sum() hdPop = (inPop * inHd).sum() out_stats.write(f'{name}: TotalPop: {tPop.round(0)}, UrbanPop: {urbPop.round(0)}, HD Pop: {hdPop.round(0)} ') out_stats.write(f'{name}: {((urbPop / tPop) * 100).round(2)}% Urban; {((hdPop / tPop) * 100).round(2)}% HD Urban ') except: print(f'Error processing {name}') print(fileDef) out_stats.write('***** Evaluate SMOD ******\n') smod_vals = [10, 11, 12, 13, 21, 22, 23, 30] inSMOD = rasterio.open(os.path.join(self.final_folder, os.path.basename(self.ghssmod_file).replace(('%s' % self.iso3.lower()), ('%s%s' % (self.iso3.lower(), self.suffix))))) smod = inSMOD.read() for pFile in self.pop_files: if ('gpo' in pFile): inPop = rasterio.open(pFile) pop = inPop.read() pop[(pop < 0)] = 0 total_pop = pop.sum() total_per = 0 for val in smod_vals: cur_smod = (smod == val).astype(int) cur_pop = (pop * cur_smod) total_curpop = cur_pop.sum() perUrban = ((total_curpop.sum() / total_pop) * 100) if (val > 20): total_per = (total_per + perUrban) out_stats.write(f'{val}: {perUrban} ') out_stats.write(f'Total Urban: {total_per} ') '3. Water mask \n ' out_stats.write('***** Evaluate Water Intersection *****\n') water_ghsl = os.path.join(self.final_folder, ('%s%s_wat.tif' % (self.iso3.lower(), self.suffix))) water_lc = os.path.join(self.final_folder, ('%s%s_wat_lc.tif' % (self.iso3.lower(), self.suffix))) inWG = rasterio.open(water_ghsl) wgData = inWG.read() wgData[(wgData == inWG.meta['nodata'])] = 0 inWLC = rasterio.open(water_lc) wlcData = inWLC.read() wlcData[(wlcData == inWLC.meta['nodata'])] = 0 combo = (wgData + wlcData) out_stats.write(f'WATER: GHSL count: {wgData.sum()}; LC count: {wlcData.sum()}; overlap: {(combo == 2).sum()} ') out_stats.write('***** Evaluate Water Population Overlap *****\n') for fileDef in pop_file_defs: pop_file = fileDef[0] urb_file = fileDef[1] hd_file = fileDef[2] name = fileDef[3] cur_pop = rasterio.open(pop_file) curP = cur_pop.read() curP[(curP == cur_pop.meta['nodata'])] = 0 urb = rasterio.open(urb_file).read() hd = rasterio.open(hd_file).read() out_stats.write(f'WATER {name} Population: TotalPop: {curP.sum().round()}, WaterPop GHSL: {(curP * wgData).sum().round()}, WaterPop LC: {(curP * wlcData).sum().round()} ') out_stats.write(f'WATER {name} Urban Cells: TotalUrban Cells: {urb.sum().round()}, WaterUrban GHSL: {(urb * wgData).sum()}, WaterUrb LC: {(urb * wlcData).sum()} ') out_stats.write(f'WATER {name} HD Cells: TotalPop: {hd.sum().round()}, WaterHD GHSL: {(hd * wgData).sum()}, WaterHD LC: {(hd * wlcData).sum()} ') for sFile in [admin_stats, commune_stats]: if os.path.exists(sFile): tPrint(sFile) file_name = os.path.basename(sFile) inD = pd.read_csv(sFile, index_col=0) out_stats.write(f'***** Summarizing {file_name} ') bad_cols = ['index', 'OBJECTID', 'WB_ADM1_CO', 'WB_ADM0_CO', 'WB_ADM2_CO', 'Shape_Leng', 'Shape_Area'] for col in inD.columns: if (not (col in bad_cols)): curD = inD[col] try: curD_sum = curD.loc[(curD > 0)].sum() out_stats.write(f'{col}: {round(curD_sum)} ') except: pass<|docstring|>Check the outputs to determine if processing worked correctly 1. compare population totals between raw, 250m and 1km data 2. Calculate urbanization rate 3. Water mask a. calculate overlap between water classes b. calculate overlap between water and population c. calculate overlap between water and urban https://ghsl.jrc.ec.europa.eu/documents/cfs01/V3/CFS_Ghana.pdf<|endoftext|>
f2b40f7bd699b11f60ae22238cb41552ca452ef706528ba10d448bce6939114a
def get_flags(config): 'Get glob flags.' flags = ((((glob.GLOBSTAR | glob.DOTGLOB) | glob.NEGATE) | glob.SPLIT) | glob.NEGATEALL) if config.get('brace_expansion', False): flags |= glob.BRACE if config.get('extended_glob', False): flags |= glob.EXTGLOB if config.get('minus_negate', True): flags |= glob.MINUSNEGATE if config.get('case_insensitive', False): flags |= glob.IGNORECASE return flags
Get glob flags.
label_bot/wildcard_labels.py
get_flags
gir-bot/do-not-merge
13
python
def get_flags(config): flags = ((((glob.GLOBSTAR | glob.DOTGLOB) | glob.NEGATE) | glob.SPLIT) | glob.NEGATEALL) if config.get('brace_expansion', False): flags |= glob.BRACE if config.get('extended_glob', False): flags |= glob.EXTGLOB if config.get('minus_negate', True): flags |= glob.MINUSNEGATE if config.get('case_insensitive', False): flags |= glob.IGNORECASE return flags
def get_flags(config): flags = ((((glob.GLOBSTAR | glob.DOTGLOB) | glob.NEGATE) | glob.SPLIT) | glob.NEGATEALL) if config.get('brace_expansion', False): flags |= glob.BRACE if config.get('extended_glob', False): flags |= glob.EXTGLOB if config.get('minus_negate', True): flags |= glob.MINUSNEGATE if config.get('case_insensitive', False): flags |= glob.IGNORECASE return flags<|docstring|>Get glob flags.<|endoftext|>
be79995a0266ec0ae5e6e58551a4d51051cdc05c9b7240e2f9fabc2f81bee3d5
def get_labels(rules, files, flags): 'Sync labels.' add_labels = {} for file in files: for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue match = False for pattern in label['patterns']: try: match = glob.globmatch(file, pattern, flags=flags) except Exception: traceback.print_exc(file=sys.stdout) match = False if match: break if match: for (index, low) in enumerate(lows): if (low not in add_labels): add_labels[low] = names[index] remove_labels = {} for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue for (index, low) in enumerate(lows): if ((low not in add_labels) and (low not in remove_labels)): remove_labels[low] = names[index] return (add_labels, remove_labels)
Sync labels.
label_bot/wildcard_labels.py
get_labels
gir-bot/do-not-merge
13
python
def get_labels(rules, files, flags): add_labels = {} for file in files: for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue match = False for pattern in label['patterns']: try: match = glob.globmatch(file, pattern, flags=flags) except Exception: traceback.print_exc(file=sys.stdout) match = False if match: break if match: for (index, low) in enumerate(lows): if (low not in add_labels): add_labels[low] = names[index] remove_labels = {} for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue for (index, low) in enumerate(lows): if ((low not in add_labels) and (low not in remove_labels)): remove_labels[low] = names[index] return (add_labels, remove_labels)
def get_labels(rules, files, flags): add_labels = {} for file in files: for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue match = False for pattern in label['patterns']: try: match = glob.globmatch(file, pattern, flags=flags) except Exception: traceback.print_exc(file=sys.stdout) match = False if match: break if match: for (index, low) in enumerate(lows): if (low not in add_labels): add_labels[low] = names[index] remove_labels = {} for label in rules: try: names = label['labels'] lows = [n.lower() for n in names] except Exception: traceback.print_exc(file=sys.stdout) continue for (index, low) in enumerate(lows): if ((low not in add_labels) and (low not in remove_labels)): remove_labels[low] = names[index] return (add_labels, remove_labels)<|docstring|>Sync labels.<|endoftext|>
7bc990d0bd7388d71b55399c76b75df02d552dad52b3678fc073ec7dc94c8377
async def wildcard_labels(event, gh, config): 'Label issues by files that have changed.' rules = config.get('rules', []) if rules: flags = get_flags(config) files = (await get_changed_files(event, gh)) (add, remove) = get_labels(rules, files, flags) (await update_issue_labels(event, gh, add, remove))
Label issues by files that have changed.
label_bot/wildcard_labels.py
wildcard_labels
gir-bot/do-not-merge
13
python
async def wildcard_labels(event, gh, config): rules = config.get('rules', []) if rules: flags = get_flags(config) files = (await get_changed_files(event, gh)) (add, remove) = get_labels(rules, files, flags) (await update_issue_labels(event, gh, add, remove))
async def wildcard_labels(event, gh, config): rules = config.get('rules', []) if rules: flags = get_flags(config) files = (await get_changed_files(event, gh)) (add, remove) = get_labels(rules, files, flags) (await update_issue_labels(event, gh, add, remove))<|docstring|>Label issues by files that have changed.<|endoftext|>
9919b2f0d3e0020bcc9aecbdbff3b394665146a3dd2cc7bc134bf2bef15b7703
async def get_changed_files(event, gh): 'Get changed files.' files = [] compare = (await gh.getitem(event.compare_url, {'base': event.base, 'head': event.head})) for file in compare['files']: files.append(file['filename']) return files
Get changed files.
label_bot/wildcard_labels.py
get_changed_files
gir-bot/do-not-merge
13
python
async def get_changed_files(event, gh): files = [] compare = (await gh.getitem(event.compare_url, {'base': event.base, 'head': event.head})) for file in compare['files']: files.append(file['filename']) return files
async def get_changed_files(event, gh): files = [] compare = (await gh.getitem(event.compare_url, {'base': event.base, 'head': event.head})) for file in compare['files']: files.append(file['filename']) return files<|docstring|>Get changed files.<|endoftext|>
9554eea4f809a94a4d38f24b7f9f99a0f6cdb7d61887a8545101aed563c23bbc
async def update_issue_labels(event, gh, add_labels, remove_labels): 'Update issue labels.' remove = [] async for name in event.get_issue_labels(gh): low = name.lower() if (low not in remove_labels): if (low in add_labels): del add_labels[low] else: remove.append(name) add = [label for label in add_labels.values()] print('WILDCARD: Removing: ', str(remove)) print('WILDCARD: Adding: ', str(add)) (await event.add_issue_labels(gh, add)) (await event.remove_issue_labels(gh, remove))
Update issue labels.
label_bot/wildcard_labels.py
update_issue_labels
gir-bot/do-not-merge
13
python
async def update_issue_labels(event, gh, add_labels, remove_labels): remove = [] async for name in event.get_issue_labels(gh): low = name.lower() if (low not in remove_labels): if (low in add_labels): del add_labels[low] else: remove.append(name) add = [label for label in add_labels.values()] print('WILDCARD: Removing: ', str(remove)) print('WILDCARD: Adding: ', str(add)) (await event.add_issue_labels(gh, add)) (await event.remove_issue_labels(gh, remove))
async def update_issue_labels(event, gh, add_labels, remove_labels): remove = [] async for name in event.get_issue_labels(gh): low = name.lower() if (low not in remove_labels): if (low in add_labels): del add_labels[low] else: remove.append(name) add = [label for label in add_labels.values()] print('WILDCARD: Removing: ', str(remove)) print('WILDCARD: Adding: ', str(add)) (await event.add_issue_labels(gh, add)) (await event.remove_issue_labels(gh, remove))<|docstring|>Update issue labels.<|endoftext|>
9690522cf42c196c5d18d3f0c335f7c09622036bdae4a4cf4008b2c0b298dc55
async def pending(event, gh): 'Set task to pending.' (await event.set_status(gh, util.EVT_PENDING, 'labels/auto-labels', 'Pending'))
Set task to pending.
label_bot/wildcard_labels.py
pending
gir-bot/do-not-merge
13
python
async def pending(event, gh): (await event.set_status(gh, util.EVT_PENDING, 'labels/auto-labels', 'Pending'))
async def pending(event, gh): (await event.set_status(gh, util.EVT_PENDING, 'labels/auto-labels', 'Pending'))<|docstring|>Set task to pending.<|endoftext|>
3e8d34060129a96b8dd7d9fae5edbb95cf60f5f33c6d322a10ae7b5267877d28
async def run(event, gh, config, **kwargs): 'Run task.' print(f'WILDCARD: {event.full_name}') try: if config.get('error', ''): raise Exception(config['error']) (await wildcard_labels(event, gh, config)) success = True except Exception: traceback.print_exc(file=sys.stdout) success = False (await event.set_status(gh, (util.EVT_SUCCESS if success else util.EVT_FAILURE), 'labels/auto-labels', ('Task completed' if success else 'Failed to complete task')))
Run task.
label_bot/wildcard_labels.py
run
gir-bot/do-not-merge
13
python
async def run(event, gh, config, **kwargs): print(f'WILDCARD: {event.full_name}') try: if config.get('error', ): raise Exception(config['error']) (await wildcard_labels(event, gh, config)) success = True except Exception: traceback.print_exc(file=sys.stdout) success = False (await event.set_status(gh, (util.EVT_SUCCESS if success else util.EVT_FAILURE), 'labels/auto-labels', ('Task completed' if success else 'Failed to complete task')))
async def run(event, gh, config, **kwargs): print(f'WILDCARD: {event.full_name}') try: if config.get('error', ): raise Exception(config['error']) (await wildcard_labels(event, gh, config)) success = True except Exception: traceback.print_exc(file=sys.stdout) success = False (await event.set_status(gh, (util.EVT_SUCCESS if success else util.EVT_FAILURE), 'labels/auto-labels', ('Task completed' if success else 'Failed to complete task')))<|docstring|>Run task.<|endoftext|>
8e354be01e6d7893ae015d4bc8d24163768aad78ce8d7a7144e52f54bd63297c
def _update_type_str(self): '\n Get type string for Label\n\n Returns\n -------\n : ~str\n\n ' self.type_str = repr(type(self.value))
Get type string for Label Returns ------- : ~str
tardis/plasma/properties/base.py
_update_type_str
wkerzendorf/tardis
1
python
def _update_type_str(self): '\n Get type string for Label\n\n Returns\n -------\n : ~str\n\n ' self.type_str = repr(type(self.value))
def _update_type_str(self): '\n Get type string for Label\n\n Returns\n -------\n : ~str\n\n ' self.type_str = repr(type(self.value))<|docstring|>Get type string for Label Returns ------- : ~str<|endoftext|>
62ac963262b237195c7af04717d7e618089b8bb8b4e7c4dcc1a7a2808679a894
def _update_inputs(self): '\n This function uses the CPython API to read the variable names from the\n `calculate`-function and makes the plasma routines easily programmable.\n ' calculate_call_signature = self.calculate.func_code.co_varnames[:self.calculate.func_code.co_argcount] self.inputs = [item for item in calculate_call_signature if (item != 'self')]
This function uses the CPython API to read the variable names from the `calculate`-function and makes the plasma routines easily programmable.
tardis/plasma/properties/base.py
_update_inputs
wkerzendorf/tardis
1
python
def _update_inputs(self): '\n This function uses the CPython API to read the variable names from the\n `calculate`-function and makes the plasma routines easily programmable.\n ' calculate_call_signature = self.calculate.func_code.co_varnames[:self.calculate.func_code.co_argcount] self.inputs = [item for item in calculate_call_signature if (item != 'self')]
def _update_inputs(self): '\n This function uses the CPython API to read the variable names from the\n `calculate`-function and makes the plasma routines easily programmable.\n ' calculate_call_signature = self.calculate.func_code.co_varnames[:self.calculate.func_code.co_argcount] self.inputs = [item for item in calculate_call_signature if (item != 'self')]<|docstring|>This function uses the CPython API to read the variable names from the `calculate`-function and makes the plasma routines easily programmable.<|endoftext|>
9e4c2182878ab61f07ab1e748cb80ae336a9bfd0155799475fbc6abbdb81a710
def update(self): '\n Updates the processing Plasma by calling the `calculate`-method with\n the required inputs\n\n :return:\n ' if (len(self.outputs) == 1): setattr(self, self.outputs[0], self.calculate(*self._get_input_values())) else: new_values = self.calculate(*self._get_input_values()) for (i, output) in enumerate(self.outputs): setattr(self, output, new_values[i])
Updates the processing Plasma by calling the `calculate`-method with the required inputs :return:
tardis/plasma/properties/base.py
update
wkerzendorf/tardis
1
python
def update(self): '\n Updates the processing Plasma by calling the `calculate`-method with\n the required inputs\n\n :return:\n ' if (len(self.outputs) == 1): setattr(self, self.outputs[0], self.calculate(*self._get_input_values())) else: new_values = self.calculate(*self._get_input_values()) for (i, output) in enumerate(self.outputs): setattr(self, output, new_values[i])
def update(self): '\n Updates the processing Plasma by calling the `calculate`-method with\n the required inputs\n\n :return:\n ' if (len(self.outputs) == 1): setattr(self, self.outputs[0], self.calculate(*self._get_input_values())) else: new_values = self.calculate(*self._get_input_values()) for (i, output) in enumerate(self.outputs): setattr(self, output, new_values[i])<|docstring|>Updates the processing Plasma by calling the `calculate`-method with the required inputs :return:<|endoftext|>
2c8ed47490a3d5b4b274e6e4979988229f4d28307a77ad9991c07c17d560e602
@abc.abstractmethod def _create_commit_lock(self, cursor): '\n Create the global lock held during commit.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()
Create the global lock held during commit. (MySQL and PostgreSQL do this differently.)
relstorage/adapters/schema.py
_create_commit_lock
dpedu/relstorage
1
python
@abc.abstractmethod def _create_commit_lock(self, cursor): '\n Create the global lock held during commit.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()
@abc.abstractmethod def _create_commit_lock(self, cursor): '\n Create the global lock held during commit.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()<|docstring|>Create the global lock held during commit. (MySQL and PostgreSQL do this differently.)<|endoftext|>
a9bf50ac586fc2c2b43ab479a07ecb996d274452c883b85fd2e31f8ccb184ff5
@abc.abstractmethod def _create_pack_lock(self, cursor): '\n Create the global lock held during pack.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()
Create the global lock held during pack. (MySQL and PostgreSQL do this differently.)
relstorage/adapters/schema.py
_create_pack_lock
dpedu/relstorage
1
python
@abc.abstractmethod def _create_pack_lock(self, cursor): '\n Create the global lock held during pack.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()
@abc.abstractmethod def _create_pack_lock(self, cursor): '\n Create the global lock held during pack.\n\n (MySQL and PostgreSQL do this differently.)\n ' raise NotImplementedError()<|docstring|>Create the global lock held during pack. (MySQL and PostgreSQL do this differently.)<|endoftext|>
e377771d9e0453d22cc6f86578bd860cc8ada129dbd6b477cf82c754fb373af5
@abc.abstractmethod def _create_transaction(self, cursor): '\n The transaction table lists all the transactions in the database.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()
The transaction table lists all the transactions in the database. This table is only used for history-preserving databases.
relstorage/adapters/schema.py
_create_transaction
dpedu/relstorage
1
python
@abc.abstractmethod def _create_transaction(self, cursor): '\n The transaction table lists all the transactions in the database.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_transaction(self, cursor): '\n The transaction table lists all the transactions in the database.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()<|docstring|>The transaction table lists all the transactions in the database. This table is only used for history-preserving databases.<|endoftext|>
bb137e976f508c47d4a55e90e205b1caa8bb1c8f49cfe69a66a77cf04175d249
@abc.abstractmethod def _create_new_oid(self, cursor): '\n Create the incrementing sequence for new OIDs.\n\n This should be the same for history free and preserving\n schemas.\n ' raise NotImplementedError()
Create the incrementing sequence for new OIDs. This should be the same for history free and preserving schemas.
relstorage/adapters/schema.py
_create_new_oid
dpedu/relstorage
1
python
@abc.abstractmethod def _create_new_oid(self, cursor): '\n Create the incrementing sequence for new OIDs.\n\n This should be the same for history free and preserving\n schemas.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_new_oid(self, cursor): '\n Create the incrementing sequence for new OIDs.\n\n This should be the same for history free and preserving\n schemas.\n ' raise NotImplementedError()<|docstring|>Create the incrementing sequence for new OIDs. This should be the same for history free and preserving schemas.<|endoftext|>
a78e9a8c5e27adc575179209f07f0311901e4d4d26ed83bd3464fd776b221194
@abc.abstractmethod def _create_object_state(self, cursor): '\n Create the table holding all object states for all transactions.\n\n If the schema is history-free, only store the current state.\n History-preserving schemas may have a NULL `object_state` to represent\n uncreation.\n ' raise NotImplementedError()
Create the table holding all object states for all transactions. If the schema is history-free, only store the current state. History-preserving schemas may have a NULL `object_state` to represent uncreation.
relstorage/adapters/schema.py
_create_object_state
dpedu/relstorage
1
python
@abc.abstractmethod def _create_object_state(self, cursor): '\n Create the table holding all object states for all transactions.\n\n If the schema is history-free, only store the current state.\n History-preserving schemas may have a NULL `object_state` to represent\n uncreation.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_object_state(self, cursor): '\n Create the table holding all object states for all transactions.\n\n If the schema is history-free, only store the current state.\n History-preserving schemas may have a NULL `object_state` to represent\n uncreation.\n ' raise NotImplementedError()<|docstring|>Create the table holding all object states for all transactions. If the schema is history-free, only store the current state. History-preserving schemas may have a NULL `object_state` to represent uncreation.<|endoftext|>
5423d5ed658d7a0b48280eb77ee3d0c6e47f67e361b46ba8e4ed95df55dbfb1d
@abc.abstractmethod def _create_blob_chunk(self, cursor): '\n Create the table holding all blob states for all transactions.\n\n If the schema is history-free, only store the current state.\n ' raise NotImplementedError()
Create the table holding all blob states for all transactions. If the schema is history-free, only store the current state.
relstorage/adapters/schema.py
_create_blob_chunk
dpedu/relstorage
1
python
@abc.abstractmethod def _create_blob_chunk(self, cursor): '\n Create the table holding all blob states for all transactions.\n\n If the schema is history-free, only store the current state.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_blob_chunk(self, cursor): '\n Create the table holding all blob states for all transactions.\n\n If the schema is history-free, only store the current state.\n ' raise NotImplementedError()<|docstring|>Create the table holding all blob states for all transactions. If the schema is history-free, only store the current state.<|endoftext|>
1dc04931f9e855d2622f4035336a4ed9bf440467f5efccfbe9dcbf10a20037a2
@abc.abstractmethod def _create_current_object(self, cursor): '\n Table that stores pointers to the current object state.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()
Table that stores pointers to the current object state. This table is only used for history-preserving databases.
relstorage/adapters/schema.py
_create_current_object
dpedu/relstorage
1
python
@abc.abstractmethod def _create_current_object(self, cursor): '\n Table that stores pointers to the current object state.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_current_object(self, cursor): '\n Table that stores pointers to the current object state.\n\n This table is only used for history-preserving databases.\n ' raise NotImplementedError()<|docstring|>Table that stores pointers to the current object state. This table is only used for history-preserving databases.<|endoftext|>
079602306d4fcc43c76f397d97eba29e8a7593981876b48ef18cda0c2afaf618
@abc.abstractmethod def _create_object_ref(self, cursor): '\n A list of referenced OIDs from each object_state. This\n table is populated as needed during packing. To prevent unnecessary\n table locking, it does not use foreign keys, which is safe because\n rows in object_state are never modified once committed, and rows are\n removed from object_state only by packing.\n ' raise NotImplementedError()
A list of referenced OIDs from each object_state. This table is populated as needed during packing. To prevent unnecessary table locking, it does not use foreign keys, which is safe because rows in object_state are never modified once committed, and rows are removed from object_state only by packing.
relstorage/adapters/schema.py
_create_object_ref
dpedu/relstorage
1
python
@abc.abstractmethod def _create_object_ref(self, cursor): '\n A list of referenced OIDs from each object_state. This\n table is populated as needed during packing. To prevent unnecessary\n table locking, it does not use foreign keys, which is safe because\n rows in object_state are never modified once committed, and rows are\n removed from object_state only by packing.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_object_ref(self, cursor): '\n A list of referenced OIDs from each object_state. This\n table is populated as needed during packing. To prevent unnecessary\n table locking, it does not use foreign keys, which is safe because\n rows in object_state are never modified once committed, and rows are\n removed from object_state only by packing.\n ' raise NotImplementedError()<|docstring|>A list of referenced OIDs from each object_state. This table is populated as needed during packing. To prevent unnecessary table locking, it does not use foreign keys, which is safe because rows in object_state are never modified once committed, and rows are removed from object_state only by packing.<|endoftext|>
c51cddf16d75cac37e8801dbc1e25537dd288a1a2fce7b758c4f5a0752be0774
@abc.abstractmethod def _create_object_refs_added(self, cursor): '\n The object_refs_added table tracks whether object_refs has been\n populated for all states in a given transaction. An entry is added\n only when the work is finished. To prevent unnecessary table locking,\n it does not use foreign keys, which is safe because object states are\n never added to a transaction once committed, and rows are removed\n from the transaction table only by packing.\n ' raise NotImplementedError()
The object_refs_added table tracks whether object_refs has been populated for all states in a given transaction. An entry is added only when the work is finished. To prevent unnecessary table locking, it does not use foreign keys, which is safe because object states are never added to a transaction once committed, and rows are removed from the transaction table only by packing.
relstorage/adapters/schema.py
_create_object_refs_added
dpedu/relstorage
1
python
@abc.abstractmethod def _create_object_refs_added(self, cursor): '\n The object_refs_added table tracks whether object_refs has been\n populated for all states in a given transaction. An entry is added\n only when the work is finished. To prevent unnecessary table locking,\n it does not use foreign keys, which is safe because object states are\n never added to a transaction once committed, and rows are removed\n from the transaction table only by packing.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_object_refs_added(self, cursor): '\n The object_refs_added table tracks whether object_refs has been\n populated for all states in a given transaction. An entry is added\n only when the work is finished. To prevent unnecessary table locking,\n it does not use foreign keys, which is safe because object states are\n never added to a transaction once committed, and rows are removed\n from the transaction table only by packing.\n ' raise NotImplementedError()<|docstring|>The object_refs_added table tracks whether object_refs has been populated for all states in a given transaction. An entry is added only when the work is finished. To prevent unnecessary table locking, it does not use foreign keys, which is safe because object states are never added to a transaction once committed, and rows are removed from the transaction table only by packing.<|endoftext|>
b124021d88da845db82b64db756394d22896619312834ff68b1a967f4b573268
@abc.abstractmethod def _create_pack_object(self, cursor): "\n pack_object contains temporary state during garbage collection: The\n list of all objects, a flag signifying whether the object should be\n kept, and a flag signifying whether the object's references have been\n visited. The keep_tid field specifies the current revision of the\n object.\n " raise NotImplementedError()
pack_object contains temporary state during garbage collection: The list of all objects, a flag signifying whether the object should be kept, and a flag signifying whether the object's references have been visited. The keep_tid field specifies the current revision of the object.
relstorage/adapters/schema.py
_create_pack_object
dpedu/relstorage
1
python
@abc.abstractmethod def _create_pack_object(self, cursor): "\n pack_object contains temporary state during garbage collection: The\n list of all objects, a flag signifying whether the object should be\n kept, and a flag signifying whether the object's references have been\n visited. The keep_tid field specifies the current revision of the\n object.\n " raise NotImplementedError()
@abc.abstractmethod def _create_pack_object(self, cursor): "\n pack_object contains temporary state during garbage collection: The\n list of all objects, a flag signifying whether the object should be\n kept, and a flag signifying whether the object's references have been\n visited. The keep_tid field specifies the current revision of the\n object.\n " raise NotImplementedError()<|docstring|>pack_object contains temporary state during garbage collection: The list of all objects, a flag signifying whether the object should be kept, and a flag signifying whether the object's references have been visited. The keep_tid field specifies the current revision of the object.<|endoftext|>
194e4cba2d7e36656bdd334187c79697a928e5ca14da3412797c7054954cf449
@abc.abstractmethod def _create_pack_state(self, cursor): '\n Temporary state during packing: the list of object states\n # to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()
Temporary state during packing: the list of object states # to pack. This is only used in history-preserving databases.
relstorage/adapters/schema.py
_create_pack_state
dpedu/relstorage
1
python
@abc.abstractmethod def _create_pack_state(self, cursor): '\n Temporary state during packing: the list of object states\n # to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_pack_state(self, cursor): '\n Temporary state during packing: the list of object states\n # to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()<|docstring|>Temporary state during packing: the list of object states # to pack. This is only used in history-preserving databases.<|endoftext|>
fa837b778215ec99e26474774547d3f5da876d50940e29ddf9dc9fc60588a4ba
@abc.abstractmethod def _create_pack_state_tid(self, cursor): '\n Temporary state during packing: the list of\n transactions that have at least one object state to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()
Temporary state during packing: the list of transactions that have at least one object state to pack. This is only used in history-preserving databases.
relstorage/adapters/schema.py
_create_pack_state_tid
dpedu/relstorage
1
python
@abc.abstractmethod def _create_pack_state_tid(self, cursor): '\n Temporary state during packing: the list of\n transactions that have at least one object state to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()
@abc.abstractmethod def _create_pack_state_tid(self, cursor): '\n Temporary state during packing: the list of\n transactions that have at least one object state to pack.\n\n This is only used in history-preserving databases.\n ' raise NotImplementedError()<|docstring|>Temporary state during packing: the list of transactions that have at least one object state to pack. This is only used in history-preserving databases.<|endoftext|>
d01d75f1bc51ccf507a6c77e5512157ac3e1a516eafdf02883b3745488a5d999
def _create_temp_store(self, _cursor): 'States that will soon be stored.' return
States that will soon be stored.
relstorage/adapters/schema.py
_create_temp_store
dpedu/relstorage
1
python
def _create_temp_store(self, _cursor): return
def _create_temp_store(self, _cursor): return<|docstring|>States that will soon be stored.<|endoftext|>
ef5d23457d4202501efa8205bf3855d203c397093e8735dfa0c2fe6ed369aef1
def _create_temp_blob_chunk(self, _cursor): '\n Temporary state during packing: a list of objects\n whose references need to be examined.\n ' return
Temporary state during packing: a list of objects whose references need to be examined.
relstorage/adapters/schema.py
_create_temp_blob_chunk
dpedu/relstorage
1
python
def _create_temp_blob_chunk(self, _cursor): '\n Temporary state during packing: a list of objects\n whose references need to be examined.\n ' return
def _create_temp_blob_chunk(self, _cursor): '\n Temporary state during packing: a list of objects\n whose references need to be examined.\n ' return<|docstring|>Temporary state during packing: a list of objects whose references need to be examined.<|endoftext|>
f2ade3e860c51478e3fa2ae169a48fb1b0e72f44538a9b79182bcf45f98e52f1
def _create_temp_undo(self, _cursor): '\n Temporary state during undo: a list of objects\n to be undone and the tid of the undone state.\n ' return
Temporary state during undo: a list of objects to be undone and the tid of the undone state.
relstorage/adapters/schema.py
_create_temp_undo
dpedu/relstorage
1
python
def _create_temp_undo(self, _cursor): '\n Temporary state during undo: a list of objects\n to be undone and the tid of the undone state.\n ' return
def _create_temp_undo(self, _cursor): '\n Temporary state during undo: a list of objects\n to be undone and the tid of the undone state.\n ' return<|docstring|>Temporary state during undo: a list of objects to be undone and the tid of the undone state.<|endoftext|>
2d0eeeb2c880ce3de1abb16f1ec4af6d54fd4028acf1ab3ec1b3121eb465871b
def _init_after_create(self, cursor): "\n Create a special '0' transaction to represent object creation. The\n '0' transaction is often referenced by object_state.prev_tid, but\n never by object_state.tid.\n\n Only in history-preserving databases.\n " if self.keep_history: stmt = "\n INSERT INTO transaction (tid, username, description)\n VALUES (0, 'system', 'special transaction for object creation');\n " self.runner.run_script(cursor, stmt)
Create a special '0' transaction to represent object creation. The '0' transaction is often referenced by object_state.prev_tid, but never by object_state.tid. Only in history-preserving databases.
relstorage/adapters/schema.py
_init_after_create
dpedu/relstorage
1
python
def _init_after_create(self, cursor): "\n Create a special '0' transaction to represent object creation. The\n '0' transaction is often referenced by object_state.prev_tid, but\n never by object_state.tid.\n\n Only in history-preserving databases.\n " if self.keep_history: stmt = "\n INSERT INTO transaction (tid, username, description)\n VALUES (0, 'system', 'special transaction for object creation');\n " self.runner.run_script(cursor, stmt)
def _init_after_create(self, cursor): "\n Create a special '0' transaction to represent object creation. The\n '0' transaction is often referenced by object_state.prev_tid, but\n never by object_state.tid.\n\n Only in history-preserving databases.\n " if self.keep_history: stmt = "\n INSERT INTO transaction (tid, username, description)\n VALUES (0, 'system', 'special transaction for object creation');\n " self.runner.run_script(cursor, stmt)<|docstring|>Create a special '0' transaction to represent object creation. The '0' transaction is often referenced by object_state.prev_tid, but never by object_state.tid. Only in history-preserving databases.<|endoftext|>
7daac01eda963767e5556d3f2ca98e1467dc8db533c5ef31784a2ebb0838c6b5
def create(self, cursor): 'Create the database tables.' for table in self.all_tables: meth = getattr(self, ('_create_' + table)) meth(cursor) self._init_after_create(cursor) tables = self.list_tables(cursor) self.check_compatibility(cursor, tables)
Create the database tables.
relstorage/adapters/schema.py
create
dpedu/relstorage
1
python
def create(self, cursor): for table in self.all_tables: meth = getattr(self, ('_create_' + table)) meth(cursor) self._init_after_create(cursor) tables = self.list_tables(cursor) self.check_compatibility(cursor, tables)
def create(self, cursor): for table in self.all_tables: meth = getattr(self, ('_create_' + table)) meth(cursor) self._init_after_create(cursor) tables = self.list_tables(cursor) self.check_compatibility(cursor, tables)<|docstring|>Create the database tables.<|endoftext|>
765cb8fd1d4b22d7a28644f0b679d1f42a9d9deb67919610ae8f70a5db0c592a
def prepare(self): 'Create the database schema if it does not already exist.' def callback(_conn, cursor): tables = self.list_tables(cursor) if ('object_state' not in tables): self.create(cursor) else: self.check_compatibility(cursor, tables) self.update_schema(cursor, tables) self.connmanager.open_and_call(callback)
Create the database schema if it does not already exist.
relstorage/adapters/schema.py
prepare
dpedu/relstorage
1
python
def prepare(self): def callback(_conn, cursor): tables = self.list_tables(cursor) if ('object_state' not in tables): self.create(cursor) else: self.check_compatibility(cursor, tables) self.update_schema(cursor, tables) self.connmanager.open_and_call(callback)
def prepare(self): def callback(_conn, cursor): tables = self.list_tables(cursor) if ('object_state' not in tables): self.create(cursor) else: self.check_compatibility(cursor, tables) self.update_schema(cursor, tables) self.connmanager.open_and_call(callback)<|docstring|>Create the database schema if it does not already exist.<|endoftext|>
bb6fb995e7f00533bb00625bb9d57d98d7c9f4d68ff8585a8bc1b94ab2890bdb
def zap_all(self, reset_oid=True, slow=False): '\n Clear all data out of the database.\n\n :keyword bool slow: If True (*not* the default) then database\n specific optimizations will be skipped and rows will simply be\n DELETEd. This is helpful when other connections might be open and\n holding some kind of locks.\n ' stmt = (self._zap_all_tbl_stmt if (not slow) else AbstractSchemaInstaller._zap_all_tbl_stmt) def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = reversed(self.all_tables) log.debug('Checking tables: %r', todo) for table in todo: log.debug('Considering table %s', table) if table.startswith('temp_'): continue if (table in existent): log.debug('Deleting from table %s...', table) cursor.execute((stmt % table)) log.debug('Done deleting from tables.') log.debug('Running init script.') self._init_after_create(cursor) log.debug('Done running init script.') if reset_oid: log.debug('Running OID reset script.') self._reset_oid(cursor) log.debug('Done running OID reset script.') self.connmanager.open_and_call(callback)
Clear all data out of the database. :keyword bool slow: If True (*not* the default) then database specific optimizations will be skipped and rows will simply be DELETEd. This is helpful when other connections might be open and holding some kind of locks.
relstorage/adapters/schema.py
zap_all
dpedu/relstorage
1
python
def zap_all(self, reset_oid=True, slow=False): '\n Clear all data out of the database.\n\n :keyword bool slow: If True (*not* the default) then database\n specific optimizations will be skipped and rows will simply be\n DELETEd. This is helpful when other connections might be open and\n holding some kind of locks.\n ' stmt = (self._zap_all_tbl_stmt if (not slow) else AbstractSchemaInstaller._zap_all_tbl_stmt) def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = reversed(self.all_tables) log.debug('Checking tables: %r', todo) for table in todo: log.debug('Considering table %s', table) if table.startswith('temp_'): continue if (table in existent): log.debug('Deleting from table %s...', table) cursor.execute((stmt % table)) log.debug('Done deleting from tables.') log.debug('Running init script.') self._init_after_create(cursor) log.debug('Done running init script.') if reset_oid: log.debug('Running OID reset script.') self._reset_oid(cursor) log.debug('Done running OID reset script.') self.connmanager.open_and_call(callback)
def zap_all(self, reset_oid=True, slow=False): '\n Clear all data out of the database.\n\n :keyword bool slow: If True (*not* the default) then database\n specific optimizations will be skipped and rows will simply be\n DELETEd. This is helpful when other connections might be open and\n holding some kind of locks.\n ' stmt = (self._zap_all_tbl_stmt if (not slow) else AbstractSchemaInstaller._zap_all_tbl_stmt) def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = reversed(self.all_tables) log.debug('Checking tables: %r', todo) for table in todo: log.debug('Considering table %s', table) if table.startswith('temp_'): continue if (table in existent): log.debug('Deleting from table %s...', table) cursor.execute((stmt % table)) log.debug('Done deleting from tables.') log.debug('Running init script.') self._init_after_create(cursor) log.debug('Done running init script.') if reset_oid: log.debug('Running OID reset script.') self._reset_oid(cursor) log.debug('Done running OID reset script.') self.connmanager.open_and_call(callback)<|docstring|>Clear all data out of the database. :keyword bool slow: If True (*not* the default) then database specific optimizations will be skipped and rows will simply be DELETEd. This is helpful when other connections might be open and holding some kind of locks.<|endoftext|>
4d4b9675605f51823727566f5514818ea63163dbd9ec716f3bc8c301841957a2
def drop_all(self): 'Drop all tables and sequences.' def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = list(self.all_tables) todo.reverse() for table in todo: if (table in existent): cursor.execute(('DROP TABLE %s' % table)) for sequence in self.list_sequences(cursor): cursor.execute(('DROP SEQUENCE %s' % sequence)) self.connmanager.open_and_call(callback)
Drop all tables and sequences.
relstorage/adapters/schema.py
drop_all
dpedu/relstorage
1
python
def drop_all(self): def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = list(self.all_tables) todo.reverse() for table in todo: if (table in existent): cursor.execute(('DROP TABLE %s' % table)) for sequence in self.list_sequences(cursor): cursor.execute(('DROP SEQUENCE %s' % sequence)) self.connmanager.open_and_call(callback)
def drop_all(self): def callback(_conn, cursor): existent = set(self.list_tables(cursor)) todo = list(self.all_tables) todo.reverse() for table in todo: if (table in existent): cursor.execute(('DROP TABLE %s' % table)) for sequence in self.list_sequences(cursor): cursor.execute(('DROP SEQUENCE %s' % sequence)) self.connmanager.open_and_call(callback)<|docstring|>Drop all tables and sequences.<|endoftext|>
2c0c7dc0ca55c5e023b558fc4c702385e149bab4474379c9e19a57ad7047d04f
def buildProtocol(self, addr): '\n Creates an instance of the ChatClient protocol. Invoked when\n the user attempts to connect to the server.\n\n Returns:\n An instance of the chat client protocol.\n ' return self.protocol(self.username, self.delegate)
Creates an instance of the ChatClient protocol. Invoked when the user attempts to connect to the server. Returns: An instance of the chat client protocol.
client/core/client/chatclientfactory.py
buildProtocol
pkrll/ChatMaster3000
0
python
def buildProtocol(self, addr): '\n Creates an instance of the ChatClient protocol. Invoked when\n the user attempts to connect to the server.\n\n Returns:\n An instance of the chat client protocol.\n ' return self.protocol(self.username, self.delegate)
def buildProtocol(self, addr): '\n Creates an instance of the ChatClient protocol. Invoked when\n the user attempts to connect to the server.\n\n Returns:\n An instance of the chat client protocol.\n ' return self.protocol(self.username, self.delegate)<|docstring|>Creates an instance of the ChatClient protocol. Invoked when the user attempts to connect to the server. Returns: An instance of the chat client protocol.<|endoftext|>
92b08e464ac529e71cb09f95f0d2659964fe47294e583ff65743897b18276a87
def clientConnectionFailed(self, connector, reason): '\n Called when the connection has failed.\n ' self.delegate.didFailConnection(reason.getErrorMessage())
Called when the connection has failed.
client/core/client/chatclientfactory.py
clientConnectionFailed
pkrll/ChatMaster3000
0
python
def clientConnectionFailed(self, connector, reason): '\n \n ' self.delegate.didFailConnection(reason.getErrorMessage())
def clientConnectionFailed(self, connector, reason): '\n \n ' self.delegate.didFailConnection(reason.getErrorMessage())<|docstring|>Called when the connection has failed.<|endoftext|>
6448fbee5f2ecea1c9d5ddf4ff242f50214ba566852458d35553d21de6102f18
def add_sdd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac vec(C) = b\n C SDD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if (len(b) != A.shape[0]): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numcon = task.getnumcon() numvar = task.getnumvar() numvar_new = ((C_size * (C_size - 1)) // 2) numcon_new = A.shape[0] D_row_idx = [] D_col_idx = [] D_vals = [] for row in range(veclen): (i, j) = k_to_ij(row, veclen) sdd_idx = sdd_index(i, j, C_size) D_row_idx += ([row] * len(sdd_idx)) D_col_idx += [((3 * k) + l) for (k, l) in sdd_idx] D_vals += [(2.0 if (l == 0) else 1.0) for (k, l) in sdd_idx] D = sp.coo_matrix((D_vals, (D_row_idx, D_col_idx)), (veclen, (3 * numvar_new))) task.appendvars((3 * numvar_new)) task.putvarboundslice(numvar, (numvar + (3 * numvar_new)), (([mosek.boundkey.fr] * 3) * numvar_new), (([0.0] * 3) * numvar_new), (([0.0] * 3) * numvar_new)) task.appendcons(numcon_new) new_A = sp.bmat([[A, spzeros(numcon_new, (numvar - A.shape[1])), (- Ac.dot(D))]]).tocsr() task.putarowslice(numcon, (numcon + numcon_new), new_A.indptr[:(- 1)], new_A.indptr[1:], new_A.indices, new_A.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b) task.appendconesseq(([mosek.conetype.rquad] * numvar_new), ([0.0] * numvar_new), ([3] * numvar_new), numvar)
add a variable C and constraints A*x - Ac vec(C) = b C SDD which with an appropriate choice of Ac makes A*x - b >= 0 if Ac is not given it is set to identity
posipoly/ppp.py
add_sdd_mosek
pettni/posipoly
1
python
def add_sdd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac vec(C) = b\n C SDD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if (len(b) != A.shape[0]): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numcon = task.getnumcon() numvar = task.getnumvar() numvar_new = ((C_size * (C_size - 1)) // 2) numcon_new = A.shape[0] D_row_idx = [] D_col_idx = [] D_vals = [] for row in range(veclen): (i, j) = k_to_ij(row, veclen) sdd_idx = sdd_index(i, j, C_size) D_row_idx += ([row] * len(sdd_idx)) D_col_idx += [((3 * k) + l) for (k, l) in sdd_idx] D_vals += [(2.0 if (l == 0) else 1.0) for (k, l) in sdd_idx] D = sp.coo_matrix((D_vals, (D_row_idx, D_col_idx)), (veclen, (3 * numvar_new))) task.appendvars((3 * numvar_new)) task.putvarboundslice(numvar, (numvar + (3 * numvar_new)), (([mosek.boundkey.fr] * 3) * numvar_new), (([0.0] * 3) * numvar_new), (([0.0] * 3) * numvar_new)) task.appendcons(numcon_new) new_A = sp.bmat([[A, spzeros(numcon_new, (numvar - A.shape[1])), (- Ac.dot(D))]]).tocsr() task.putarowslice(numcon, (numcon + numcon_new), new_A.indptr[:(- 1)], new_A.indptr[1:], new_A.indices, new_A.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b) task.appendconesseq(([mosek.conetype.rquad] * numvar_new), ([0.0] * numvar_new), ([3] * numvar_new), numvar)
def add_sdd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac vec(C) = b\n C SDD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if (len(b) != A.shape[0]): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numcon = task.getnumcon() numvar = task.getnumvar() numvar_new = ((C_size * (C_size - 1)) // 2) numcon_new = A.shape[0] D_row_idx = [] D_col_idx = [] D_vals = [] for row in range(veclen): (i, j) = k_to_ij(row, veclen) sdd_idx = sdd_index(i, j, C_size) D_row_idx += ([row] * len(sdd_idx)) D_col_idx += [((3 * k) + l) for (k, l) in sdd_idx] D_vals += [(2.0 if (l == 0) else 1.0) for (k, l) in sdd_idx] D = sp.coo_matrix((D_vals, (D_row_idx, D_col_idx)), (veclen, (3 * numvar_new))) task.appendvars((3 * numvar_new)) task.putvarboundslice(numvar, (numvar + (3 * numvar_new)), (([mosek.boundkey.fr] * 3) * numvar_new), (([0.0] * 3) * numvar_new), (([0.0] * 3) * numvar_new)) task.appendcons(numcon_new) new_A = sp.bmat([[A, spzeros(numcon_new, (numvar - A.shape[1])), (- Ac.dot(D))]]).tocsr() task.putarowslice(numcon, (numcon + numcon_new), new_A.indptr[:(- 1)], new_A.indptr[1:], new_A.indices, new_A.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b) task.appendconesseq(([mosek.conetype.rquad] * numvar_new), ([0.0] * numvar_new), ([3] * numvar_new), numvar)<|docstring|>add a variable C and constraints A*x - Ac vec(C) = b C SDD which with an appropriate choice of Ac makes A*x - b >= 0 if Ac is not given it is set to identity<|endoftext|>
cb2fddde8cf7bbb6fd3c7f25edca2d37378e932e6d2309325180cafbd651197e
def add_psd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac*vec(C) = b\n C PSD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if ((len(b) != A.shape[0]) or (task.getnumvar() != A.shape[1])): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numbarvar = task.getnumbarvar() numcon = task.getnumcon() task.appendbarvars([C_size]) numcon_new = len(b) task.appendcons(numcon_new) Ac_csr = Ac.tocsr() for it in range(numcon_new): it_row = Ac_csr.getrow(it) if (it_row.nnz > 0): (i_list, j_list) = zip(*[k_to_ij(k, veclen) for k in it_row.indices]) val = [(it_row.data[l] if (i_list[l] == j_list[l]) else (it_row.data[l] / 2)) for l in range(len(i_list))] mat_it = task.appendsparsesymmat(C_size, j_list, i_list, (- np.array(val))) task.putbaraij((numcon + it), numbarvar, [mat_it], [1.0]) A_csr = A.tocsr() task.putarowslice(numcon, (numcon + numcon_new), A_csr.indptr[:(- 1)], A_csr.indptr[1:], A_csr.indices, A_csr.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b)
add a variable C and constraints A*x - Ac*vec(C) = b C PSD which with an appropriate choice of Ac makes A*x - b >= 0 if Ac is not given it is set to identity
posipoly/ppp.py
add_psd_mosek
pettni/posipoly
1
python
def add_psd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac*vec(C) = b\n C PSD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if ((len(b) != A.shape[0]) or (task.getnumvar() != A.shape[1])): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numbarvar = task.getnumbarvar() numcon = task.getnumcon() task.appendbarvars([C_size]) numcon_new = len(b) task.appendcons(numcon_new) Ac_csr = Ac.tocsr() for it in range(numcon_new): it_row = Ac_csr.getrow(it) if (it_row.nnz > 0): (i_list, j_list) = zip(*[k_to_ij(k, veclen) for k in it_row.indices]) val = [(it_row.data[l] if (i_list[l] == j_list[l]) else (it_row.data[l] / 2)) for l in range(len(i_list))] mat_it = task.appendsparsesymmat(C_size, j_list, i_list, (- np.array(val))) task.putbaraij((numcon + it), numbarvar, [mat_it], [1.0]) A_csr = A.tocsr() task.putarowslice(numcon, (numcon + numcon_new), A_csr.indptr[:(- 1)], A_csr.indptr[1:], A_csr.indices, A_csr.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b)
def add_psd_mosek(task, A, b, Ac=None): ' \n add a variable C and constraints\n A*x - Ac*vec(C) = b\n C PSD\n which with an appropriate choice of Ac makes A*x - b >= 0\n\n if Ac is not given it is set to identity\n ' if (Ac is None): n_Ac = A.shape[0] Ac = sp.coo_matrix((np.ones(n_Ac), (range(n_Ac), range(n_Ac))), (n_Ac, n_Ac)) if ((len(b) != A.shape[0]) or (task.getnumvar() != A.shape[1])): raise Exception('invalid size of A') if (len(b) != Ac.shape[0]): raise Exception('invalid size of Ac') veclen = Ac.shape[1] C_size = veclen_to_matsize(veclen) numbarvar = task.getnumbarvar() numcon = task.getnumcon() task.appendbarvars([C_size]) numcon_new = len(b) task.appendcons(numcon_new) Ac_csr = Ac.tocsr() for it in range(numcon_new): it_row = Ac_csr.getrow(it) if (it_row.nnz > 0): (i_list, j_list) = zip(*[k_to_ij(k, veclen) for k in it_row.indices]) val = [(it_row.data[l] if (i_list[l] == j_list[l]) else (it_row.data[l] / 2)) for l in range(len(i_list))] mat_it = task.appendsparsesymmat(C_size, j_list, i_list, (- np.array(val))) task.putbaraij((numcon + it), numbarvar, [mat_it], [1.0]) A_csr = A.tocsr() task.putarowslice(numcon, (numcon + numcon_new), A_csr.indptr[:(- 1)], A_csr.indptr[1:], A_csr.indices, A_csr.data) task.putconboundslice(numcon, (numcon + numcon_new), ([mosek.boundkey.fx] * numcon_new), b, b)<|docstring|>add a variable C and constraints A*x - Ac*vec(C) = b C PSD which with an appropriate choice of Ac makes A*x - b >= 0 if Ac is not given it is set to identity<|endoftext|>
0fc85e4a1731655fc8ba93bd9f0a20aaa75dc2ba63e6ecf793789a868db6ffd2
def is_dd(A): " Returns 'True' if A is dd (diagonally dominant), 'False' otherwise " epsilon = 1e-10 A_arr = np.array(A) if (A_arr.shape[0] != A_arr.shape[1]): return False n = A_arr.shape[0] for i in range(n): if (not ((A[(i, i)] + epsilon) >= sum(np.abs(A[(i, [j for j in range(n) if (i != j)])])))): return False return True
Returns 'True' if A is dd (diagonally dominant), 'False' otherwise
posipoly/ppp.py
is_dd
pettni/posipoly
1
python
def is_dd(A): " " epsilon = 1e-10 A_arr = np.array(A) if (A_arr.shape[0] != A_arr.shape[1]): return False n = A_arr.shape[0] for i in range(n): if (not ((A[(i, i)] + epsilon) >= sum(np.abs(A[(i, [j for j in range(n) if (i != j)])])))): return False return True
def is_dd(A): " " epsilon = 1e-10 A_arr = np.array(A) if (A_arr.shape[0] != A_arr.shape[1]): return False n = A_arr.shape[0] for i in range(n): if (not ((A[(i, i)] + epsilon) >= sum(np.abs(A[(i, [j for j in range(n) if (i != j)])])))): return False return True<|docstring|>Returns 'True' if A is dd (diagonally dominant), 'False' otherwise<|endoftext|>
a45dde8a30079ec4b46c9a921a36b68070c2ad6a47b8ab07162e15ccc98044ba
def sdd_index(i, j, n): " An n x n sdd matrix A can be written as A = sum Mij.\n Given Mij's stored as a (n-1)*n/2 x 3 matrix, where each row represents a 2x2 symmetric matrix, return\n the indices i_s, j_s such that A_ij = sum_s Mij(i_s, j_s) " num_vars = int(((n * (n - 1)) / 2)) if (i == j): return [[ij_to_k(min(i, l), (max(i, l) - 1), num_vars), (0 if (i < l) else 1)] for l in range(n) if (l != i)] else: return [[ij_to_k(min(i, j), (max(i, j) - 1), num_vars), 2]]
An n x n sdd matrix A can be written as A = sum Mij. Given Mij's stored as a (n-1)*n/2 x 3 matrix, where each row represents a 2x2 symmetric matrix, return the indices i_s, j_s such that A_ij = sum_s Mij(i_s, j_s)
posipoly/ppp.py
sdd_index
pettni/posipoly
1
python
def sdd_index(i, j, n): " An n x n sdd matrix A can be written as A = sum Mij.\n Given Mij's stored as a (n-1)*n/2 x 3 matrix, where each row represents a 2x2 symmetric matrix, return\n the indices i_s, j_s such that A_ij = sum_s Mij(i_s, j_s) " num_vars = int(((n * (n - 1)) / 2)) if (i == j): return [[ij_to_k(min(i, l), (max(i, l) - 1), num_vars), (0 if (i < l) else 1)] for l in range(n) if (l != i)] else: return [[ij_to_k(min(i, j), (max(i, j) - 1), num_vars), 2]]
def sdd_index(i, j, n): " An n x n sdd matrix A can be written as A = sum Mij.\n Given Mij's stored as a (n-1)*n/2 x 3 matrix, where each row represents a 2x2 symmetric matrix, return\n the indices i_s, j_s such that A_ij = sum_s Mij(i_s, j_s) " num_vars = int(((n * (n - 1)) / 2)) if (i == j): return [[ij_to_k(min(i, l), (max(i, l) - 1), num_vars), (0 if (i < l) else 1)] for l in range(n) if (l != i)] else: return [[ij_to_k(min(i, j), (max(i, j) - 1), num_vars), 2]]<|docstring|>An n x n sdd matrix A can be written as A = sum Mij. Given Mij's stored as a (n-1)*n/2 x 3 matrix, where each row represents a 2x2 symmetric matrix, return the indices i_s, j_s such that A_ij = sum_s Mij(i_s, j_s)<|endoftext|>
cbebc5bd59a8aa40ff4d25bfcdabf36e9ae33676510a9e83b81244682f5d4f98
@property def varnames(self): 'get names of all variables' return list(self.varinfo.keys())
get names of all variables
posipoly/ppp.py
varnames
pettni/posipoly
1
python
@property def varnames(self): return list(self.varinfo.keys())
@property def varnames(self): return list(self.varinfo.keys())<|docstring|>get names of all variables<|endoftext|>
d3d81656bf42a8b395ade257869501be65a0dd128b9aa374c9b01596a95c17d1
@property def varsizes(self): 'get coefficient sizes of all polynomial variables' return [self.varsize(name) for name in self.varinfo.keys()]
get coefficient sizes of all polynomial variables
posipoly/ppp.py
varsizes
pettni/posipoly
1
python
@property def varsizes(self): return [self.varsize(name) for name in self.varinfo.keys()]
@property def varsizes(self): return [self.varsize(name) for name in self.varinfo.keys()]<|docstring|>get coefficient sizes of all polynomial variables<|endoftext|>
fa3d4081682b6eaf1b4b028a63ff5349e74d3e90547589d8b5e91c86e9bc981f
@property def numvar(self): 'total number of coefficient variables' return sum(self.varsizes)
total number of coefficient variables
posipoly/ppp.py
numvar
pettni/posipoly
1
python
@property def numvar(self): return sum(self.varsizes)
@property def numvar(self): return sum(self.varsizes)<|docstring|>total number of coefficient variables<|endoftext|>
0ad6cfb6ea33e9bc0dcf050022669720a424abb268d609b832e96f7220d44ea2
@property def numcon(self): 'total number of constraints' return (self.Aeq.shape[0] + self.Aiq.shape[0])
total number of constraints
posipoly/ppp.py
numcon
pettni/posipoly
1
python
@property def numcon(self): return (self.Aeq.shape[0] + self.Aiq.shape[0])
@property def numcon(self): return (self.Aeq.shape[0] + self.Aiq.shape[0])<|docstring|>total number of constraints<|endoftext|>
7371b2fbdc61e4f61a898b46a8a49fcec86f33aacb43e4005bedc64f6b9d20b2
def varsize(self, varname): 'get size of variable "varname"' if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'pp'): num_mon = count_monomials_leq(n, int(ceil((float(d) / 2)))) return ((num_mon * (num_mon + 1)) // 2) if (self.varinfo[varname][2] == 'coef'): return count_monomials_leq(n, d)
get size of variable "varname"
posipoly/ppp.py
varsize
pettni/posipoly
1
python
def varsize(self, varname): if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'pp'): num_mon = count_monomials_leq(n, int(ceil((float(d) / 2)))) return ((num_mon * (num_mon + 1)) // 2) if (self.varinfo[varname][2] == 'coef'): return count_monomials_leq(n, d)
def varsize(self, varname): if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'pp'): num_mon = count_monomials_leq(n, int(ceil((float(d) / 2)))) return ((num_mon * (num_mon + 1)) // 2) if (self.varinfo[varname][2] == 'coef'): return count_monomials_leq(n, d)<|docstring|>get size of variable "varname"<|endoftext|>
28f9a7fdd3be61cc7e83a49acfe6d35e500257d72ee81628d439c8c23a3d2fb1
def varpos(self, varname): 'return starting position of variable' if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) ret = 0 for name in self.varnames: if (name == varname): break ret += self.varsize(name) return ret
return starting position of variable
posipoly/ppp.py
varpos
pettni/posipoly
1
python
def varpos(self, varname): if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) ret = 0 for name in self.varnames: if (name == varname): break ret += self.varsize(name) return ret
def varpos(self, varname): if (varname not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(varname)) ret = 0 for name in self.varnames: if (name == varname): break ret += self.varsize(name) return ret<|docstring|>return starting position of variable<|endoftext|>
91280ab9726b13ad5d00ee5794bb3a156f86b9fa3cdb32766bddc08d50ad6d7b
def add_constraint(self, Aop_dict, b, tp): " \n add a constraint to problem of form\n T1 var1 + T2 var2 <= b if tp=='iq' (coefficient-wise inequality)\n T1 var1 + T2 var2 = b if tp=='eq' (coefficient-wise equality)\n T1 var1 + T2 var2 - b pp if tp=='pp' (positive polynomial)\n\n Parameters\n ----------\n Aop_dict : dict\n dict with values PTrans. \n Variables that are not present as keys are assumed \n to have the zero operator.\n b : Polynomial\n Right-Hand side of constraint\n tp : {'eq', 'iq', 'pp'}\n Type of constraint ('eq'uality, 'in'equality, or 'p'ositive 'p'olynomial).\n \n Example\n ----------\n >>> prob = PPP({'x': (2, 2, 'gram'), 'y': (2, 3, 'gram')})\n >>> T = PTrans.eye(2,2)\n >>> b = Polynomial({(1,2): 1}) # x * y**2\n >>> prob.add_constraint({'x': T}, b, 'eq')\n " if (tp not in ['eq', 'iq', 'pp']): raise Exception('tp must be "eq" or "iq" or "pp"') for name in Aop_dict.keys(): if (name not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(name)) if (not all(((Aop_dict[name].n0 == self.varinfo[name][0]) for name in Aop_dict.keys()))): raise Exception('PTrans initial dimensions do not agree') if (not all(((Aop_dict[name].d0 >= self.varinfo[name][1]) for name in Aop_dict.keys()))): raise Exception('PTrans initial degrees too low') n1_list = [Aop_dict[name].n1 for name in Aop_dict.keys()] d1_list = [Aop_dict[name].d1 for name in Aop_dict.keys()] if ((max(n1_list) != min(n1_list)) or (max(d1_list) != min(d1_list))): raise Exception('final degrees and dimensions must match') if ((n1_list[0] != b.n) or (d1_list[0] < b.d)): raise Exception('must have b.n = Aop.n1 and b.d <= Aop.d1 for all Aop') if ((tp == 'iq') and (b.d > 0)): print('Warning: adding coefficient-wise inequality constraint. make sure this is what you want') n = n1_list[0] d = d1_list[0] self.constraints.append((Aop_dict, b, n, d, tp))
add a constraint to problem of form T1 var1 + T2 var2 <= b if tp=='iq' (coefficient-wise inequality) T1 var1 + T2 var2 = b if tp=='eq' (coefficient-wise equality) T1 var1 + T2 var2 - b pp if tp=='pp' (positive polynomial) Parameters ---------- Aop_dict : dict dict with values PTrans. Variables that are not present as keys are assumed to have the zero operator. b : Polynomial Right-Hand side of constraint tp : {'eq', 'iq', 'pp'} Type of constraint ('eq'uality, 'in'equality, or 'p'ositive 'p'olynomial). Example ---------- >>> prob = PPP({'x': (2, 2, 'gram'), 'y': (2, 3, 'gram')}) >>> T = PTrans.eye(2,2) >>> b = Polynomial({(1,2): 1}) # x * y**2 >>> prob.add_constraint({'x': T}, b, 'eq')
posipoly/ppp.py
add_constraint
pettni/posipoly
1
python
def add_constraint(self, Aop_dict, b, tp): " \n add a constraint to problem of form\n T1 var1 + T2 var2 <= b if tp=='iq' (coefficient-wise inequality)\n T1 var1 + T2 var2 = b if tp=='eq' (coefficient-wise equality)\n T1 var1 + T2 var2 - b pp if tp=='pp' (positive polynomial)\n\n Parameters\n ----------\n Aop_dict : dict\n dict with values PTrans. \n Variables that are not present as keys are assumed \n to have the zero operator.\n b : Polynomial\n Right-Hand side of constraint\n tp : {'eq', 'iq', 'pp'}\n Type of constraint ('eq'uality, 'in'equality, or 'p'ositive 'p'olynomial).\n \n Example\n ----------\n >>> prob = PPP({'x': (2, 2, 'gram'), 'y': (2, 3, 'gram')})\n >>> T = PTrans.eye(2,2)\n >>> b = Polynomial({(1,2): 1}) # x * y**2\n >>> prob.add_constraint({'x': T}, b, 'eq')\n " if (tp not in ['eq', 'iq', 'pp']): raise Exception('tp must be "eq" or "iq" or "pp"') for name in Aop_dict.keys(): if (name not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(name)) if (not all(((Aop_dict[name].n0 == self.varinfo[name][0]) for name in Aop_dict.keys()))): raise Exception('PTrans initial dimensions do not agree') if (not all(((Aop_dict[name].d0 >= self.varinfo[name][1]) for name in Aop_dict.keys()))): raise Exception('PTrans initial degrees too low') n1_list = [Aop_dict[name].n1 for name in Aop_dict.keys()] d1_list = [Aop_dict[name].d1 for name in Aop_dict.keys()] if ((max(n1_list) != min(n1_list)) or (max(d1_list) != min(d1_list))): raise Exception('final degrees and dimensions must match') if ((n1_list[0] != b.n) or (d1_list[0] < b.d)): raise Exception('must have b.n = Aop.n1 and b.d <= Aop.d1 for all Aop') if ((tp == 'iq') and (b.d > 0)): print('Warning: adding coefficient-wise inequality constraint. make sure this is what you want') n = n1_list[0] d = d1_list[0] self.constraints.append((Aop_dict, b, n, d, tp))
def add_constraint(self, Aop_dict, b, tp): " \n add a constraint to problem of form\n T1 var1 + T2 var2 <= b if tp=='iq' (coefficient-wise inequality)\n T1 var1 + T2 var2 = b if tp=='eq' (coefficient-wise equality)\n T1 var1 + T2 var2 - b pp if tp=='pp' (positive polynomial)\n\n Parameters\n ----------\n Aop_dict : dict\n dict with values PTrans. \n Variables that are not present as keys are assumed \n to have the zero operator.\n b : Polynomial\n Right-Hand side of constraint\n tp : {'eq', 'iq', 'pp'}\n Type of constraint ('eq'uality, 'in'equality, or 'p'ositive 'p'olynomial).\n \n Example\n ----------\n >>> prob = PPP({'x': (2, 2, 'gram'), 'y': (2, 3, 'gram')})\n >>> T = PTrans.eye(2,2)\n >>> b = Polynomial({(1,2): 1}) # x * y**2\n >>> prob.add_constraint({'x': T}, b, 'eq')\n " if (tp not in ['eq', 'iq', 'pp']): raise Exception('tp must be "eq" or "iq" or "pp"') for name in Aop_dict.keys(): if (name not in self.varinfo.keys()): raise Exception('unknown variable {}'.format(name)) if (not all(((Aop_dict[name].n0 == self.varinfo[name][0]) for name in Aop_dict.keys()))): raise Exception('PTrans initial dimensions do not agree') if (not all(((Aop_dict[name].d0 >= self.varinfo[name][1]) for name in Aop_dict.keys()))): raise Exception('PTrans initial degrees too low') n1_list = [Aop_dict[name].n1 for name in Aop_dict.keys()] d1_list = [Aop_dict[name].d1 for name in Aop_dict.keys()] if ((max(n1_list) != min(n1_list)) or (max(d1_list) != min(d1_list))): raise Exception('final degrees and dimensions must match') if ((n1_list[0] != b.n) or (d1_list[0] < b.d)): raise Exception('must have b.n = Aop.n1 and b.d <= Aop.d1 for all Aop') if ((tp == 'iq') and (b.d > 0)): print('Warning: adding coefficient-wise inequality constraint. make sure this is what you want') n = n1_list[0] d = d1_list[0] self.constraints.append((Aop_dict, b, n, d, tp))<|docstring|>add a constraint to problem of form T1 var1 + T2 var2 <= b if tp=='iq' (coefficient-wise inequality) T1 var1 + T2 var2 = b if tp=='eq' (coefficient-wise equality) T1 var1 + T2 var2 - b pp if tp=='pp' (positive polynomial) Parameters ---------- Aop_dict : dict dict with values PTrans. Variables that are not present as keys are assumed to have the zero operator. b : Polynomial Right-Hand side of constraint tp : {'eq', 'iq', 'pp'} Type of constraint ('eq'uality, 'in'equality, or 'p'ositive 'p'olynomial). Example ---------- >>> prob = PPP({'x': (2, 2, 'gram'), 'y': (2, 3, 'gram')}) >>> T = PTrans.eye(2,2) >>> b = Polynomial({(1,2): 1}) # x * y**2 >>> prob.add_constraint({'x': T}, b, 'eq')<|endoftext|>
dd05a93e733982e027343b154e2c8c98e98c0a398e1541b5ab824f2bd4fef1dc
def set_objective(self, c_dict): " \n add objective to problem \n\n Parameters\n ----------\n c_dict : dict\n keys: varnames\n values: PTrans with final degree 0 (scalar) or array_like\n\n Example\n ----------\n >>> T = PTrans.integrate(n,d,dims,boxes) # results in scalar\n >>> prob.add_constraint({'a': [0,1 ], 'b': T} )\n " if (not (type(c_dict) is dict)): raise Exception('c_dict must be dict') def pick_vec(varname): if (varname not in c_dict.keys()): return np.zeros(self.varsize(varname)) if (type(c_dict[varname]) is PTrans): if (not (c_dict[varname].d1 == 0)): raise Exception('cost for {} not scalar'.format(varname)) if (self.varinfo[varname][2] == 'pp'): return c_dict[varname].Acg().todense().getA1() else: return c_dict[varname].Acc().todense().getA1() else: if (len(c_dict[varname]) != self.varsize(varname)): raise Exception('cost for {} is wrong size'.format(varname)) return c_dict[varname] self.c = np.hstack([pick_vec(varname) for varname in self.varnames]) if (not (len(self.c) == self.numvar)): raise Exception('wrong size of objective')
add objective to problem Parameters ---------- c_dict : dict keys: varnames values: PTrans with final degree 0 (scalar) or array_like Example ---------- >>> T = PTrans.integrate(n,d,dims,boxes) # results in scalar >>> prob.add_constraint({'a': [0,1 ], 'b': T} )
posipoly/ppp.py
set_objective
pettni/posipoly
1
python
def set_objective(self, c_dict): " \n add objective to problem \n\n Parameters\n ----------\n c_dict : dict\n keys: varnames\n values: PTrans with final degree 0 (scalar) or array_like\n\n Example\n ----------\n >>> T = PTrans.integrate(n,d,dims,boxes) # results in scalar\n >>> prob.add_constraint({'a': [0,1 ], 'b': T} )\n " if (not (type(c_dict) is dict)): raise Exception('c_dict must be dict') def pick_vec(varname): if (varname not in c_dict.keys()): return np.zeros(self.varsize(varname)) if (type(c_dict[varname]) is PTrans): if (not (c_dict[varname].d1 == 0)): raise Exception('cost for {} not scalar'.format(varname)) if (self.varinfo[varname][2] == 'pp'): return c_dict[varname].Acg().todense().getA1() else: return c_dict[varname].Acc().todense().getA1() else: if (len(c_dict[varname]) != self.varsize(varname)): raise Exception('cost for {} is wrong size'.format(varname)) return c_dict[varname] self.c = np.hstack([pick_vec(varname) for varname in self.varnames]) if (not (len(self.c) == self.numvar)): raise Exception('wrong size of objective')
def set_objective(self, c_dict): " \n add objective to problem \n\n Parameters\n ----------\n c_dict : dict\n keys: varnames\n values: PTrans with final degree 0 (scalar) or array_like\n\n Example\n ----------\n >>> T = PTrans.integrate(n,d,dims,boxes) # results in scalar\n >>> prob.add_constraint({'a': [0,1 ], 'b': T} )\n " if (not (type(c_dict) is dict)): raise Exception('c_dict must be dict') def pick_vec(varname): if (varname not in c_dict.keys()): return np.zeros(self.varsize(varname)) if (type(c_dict[varname]) is PTrans): if (not (c_dict[varname].d1 == 0)): raise Exception('cost for {} not scalar'.format(varname)) if (self.varinfo[varname][2] == 'pp'): return c_dict[varname].Acg().todense().getA1() else: return c_dict[varname].Acc().todense().getA1() else: if (len(c_dict[varname]) != self.varsize(varname)): raise Exception('cost for {} is wrong size'.format(varname)) return c_dict[varname] self.c = np.hstack([pick_vec(varname) for varname in self.varnames]) if (not (len(self.c) == self.numvar)): raise Exception('wrong size of objective')<|docstring|>add objective to problem Parameters ---------- c_dict : dict keys: varnames values: PTrans with final degree 0 (scalar) or array_like Example ---------- >>> T = PTrans.integrate(n,d,dims,boxes) # results in scalar >>> prob.add_constraint({'a': [0,1 ], 'b': T} )<|endoftext|>
349f0a4a43468da8b5f8438e4338984cf42b1afdb075de9ca7f4a2e1402fe801
def get_bmat(self, Aop_dict, numcon): ' \n concatenate matrices in dict in variable order, fill in with zero matrix\n for those variables that do not appear\n ' def pick_mat(varname): if (varname not in Aop_dict.keys()): return spzeros(numcon, self.varsize(varname)) if (self.varinfo[varname][2] == 'pp'): return Aop_dict[varname].Acg() if (self.varinfo[varname][2] == 'coef'): return Aop_dict[varname].Acc() return sp.bmat([[pick_mat(vn) for vn in self.varnames]])
concatenate matrices in dict in variable order, fill in with zero matrix for those variables that do not appear
posipoly/ppp.py
get_bmat
pettni/posipoly
1
python
def get_bmat(self, Aop_dict, numcon): ' \n concatenate matrices in dict in variable order, fill in with zero matrix\n for those variables that do not appear\n ' def pick_mat(varname): if (varname not in Aop_dict.keys()): return spzeros(numcon, self.varsize(varname)) if (self.varinfo[varname][2] == 'pp'): return Aop_dict[varname].Acg() if (self.varinfo[varname][2] == 'coef'): return Aop_dict[varname].Acc() return sp.bmat([[pick_mat(vn) for vn in self.varnames]])
def get_bmat(self, Aop_dict, numcon): ' \n concatenate matrices in dict in variable order, fill in with zero matrix\n for those variables that do not appear\n ' def pick_mat(varname): if (varname not in Aop_dict.keys()): return spzeros(numcon, self.varsize(varname)) if (self.varinfo[varname][2] == 'pp'): return Aop_dict[varname].Acg() if (self.varinfo[varname][2] == 'coef'): return Aop_dict[varname].Acc() return sp.bmat([[pick_mat(vn) for vn in self.varnames]])<|docstring|>concatenate matrices in dict in variable order, fill in with zero matrix for those variables that do not appear<|endoftext|>
9eb282b70fc131004fc47fee317236387c277e46bcbf9068b816f600c0a9fc8c
def solve(self, pp_cone): " \n solve Positive Polynomial Program \n\n Parameters\n ----------\n pp_cone : {'psd', 'sdd'}\n cone for positivity constraints\n \n Returns\n ----------\n sol : solution vector\n sta : status\n " Aeq = sp.coo_matrix((0, self.numvar)) Aiq = sp.coo_matrix((0, self.numvar)) beq = np.zeros(0) biq = np.zeros(0) for (Aop_dict, b, n, d, tp) in self.constraints: Amat = self.get_bmat(Aop_dict, count_monomials_leq(n, d)) if (tp == 'eq'): Aeq = sp.bmat([[Aeq], [Amat]]) beq = np.hstack([beq, b.mon_coefs(d)]) if (tp == 'iq'): Aiq = sp.bmat([[Aiq], [Amat]]) biq = np.hstack([biq, b.mon_coefs(d)]) numcon_iq = Aiq.shape[0] numcon_eq = Aeq.shape[0] env = mosek.Env() task = env.Task(0, 0) task.appendvars(self.numvar) task.putvarboundslice(0, self.numvar, ([mosek.boundkey.fr] * self.numvar), ([0.0] * self.numvar), ([0.0] * self.numvar)) task.putcslice(0, self.numvar, self.c) task.putobjsense(mosek.objsense.minimize) A = sp.bmat([[Aeq], [Aiq]]) task.appendcons((numcon_eq + numcon_iq)) task.putaijlist(A.row, A.col, A.data) task.putconboundslice(0, (numcon_eq + numcon_iq), (([mosek.boundkey.fx] * numcon_eq) + ([mosek.boundkey.up] * numcon_iq)), (list(beq) + ([0.0] * numcon_iq)), (list(beq) + list(biq))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): Asp = speye(self.varsize(varname), self.varpos(varname), self.numvar) if (pp_cone == 'psd'): add_psd_mosek(task, Asp, np.zeros(self.varsize(varname))) if (pp_cone == 'sdd'): add_sdd_mosek(task, Asp, np.zeros(self.varsize(varname))) for (Aop_dict, b_pol, n, d, tp) in self.constraints: if (tp == 'pp'): if (pp_cone == 'psd'): add_psd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) if (pp_cone == 'sdd'): add_sdd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) print('optimizing...') t_start = time.clock() task.optimize() print('solved in {:.2f}s'.format((time.clock() - t_start))) solsta = task.getsolsta(mosek.soltype.itr) print(solsta) if (solsta == mosek.solsta.optimal): sol = ([0.0] * self.numvar) task.getxxslice(mosek.soltype.itr, 0, self.numvar, sol) self.sol = sol self.check_sol(Aiq, biq, Aeq, beq) return (sol, solsta) else: return (None, solsta)
solve Positive Polynomial Program Parameters ---------- pp_cone : {'psd', 'sdd'} cone for positivity constraints Returns ---------- sol : solution vector sta : status
posipoly/ppp.py
solve
pettni/posipoly
1
python
def solve(self, pp_cone): " \n solve Positive Polynomial Program \n\n Parameters\n ----------\n pp_cone : {'psd', 'sdd'}\n cone for positivity constraints\n \n Returns\n ----------\n sol : solution vector\n sta : status\n " Aeq = sp.coo_matrix((0, self.numvar)) Aiq = sp.coo_matrix((0, self.numvar)) beq = np.zeros(0) biq = np.zeros(0) for (Aop_dict, b, n, d, tp) in self.constraints: Amat = self.get_bmat(Aop_dict, count_monomials_leq(n, d)) if (tp == 'eq'): Aeq = sp.bmat([[Aeq], [Amat]]) beq = np.hstack([beq, b.mon_coefs(d)]) if (tp == 'iq'): Aiq = sp.bmat([[Aiq], [Amat]]) biq = np.hstack([biq, b.mon_coefs(d)]) numcon_iq = Aiq.shape[0] numcon_eq = Aeq.shape[0] env = mosek.Env() task = env.Task(0, 0) task.appendvars(self.numvar) task.putvarboundslice(0, self.numvar, ([mosek.boundkey.fr] * self.numvar), ([0.0] * self.numvar), ([0.0] * self.numvar)) task.putcslice(0, self.numvar, self.c) task.putobjsense(mosek.objsense.minimize) A = sp.bmat([[Aeq], [Aiq]]) task.appendcons((numcon_eq + numcon_iq)) task.putaijlist(A.row, A.col, A.data) task.putconboundslice(0, (numcon_eq + numcon_iq), (([mosek.boundkey.fx] * numcon_eq) + ([mosek.boundkey.up] * numcon_iq)), (list(beq) + ([0.0] * numcon_iq)), (list(beq) + list(biq))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): Asp = speye(self.varsize(varname), self.varpos(varname), self.numvar) if (pp_cone == 'psd'): add_psd_mosek(task, Asp, np.zeros(self.varsize(varname))) if (pp_cone == 'sdd'): add_sdd_mosek(task, Asp, np.zeros(self.varsize(varname))) for (Aop_dict, b_pol, n, d, tp) in self.constraints: if (tp == 'pp'): if (pp_cone == 'psd'): add_psd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) if (pp_cone == 'sdd'): add_sdd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) print('optimizing...') t_start = time.clock() task.optimize() print('solved in {:.2f}s'.format((time.clock() - t_start))) solsta = task.getsolsta(mosek.soltype.itr) print(solsta) if (solsta == mosek.solsta.optimal): sol = ([0.0] * self.numvar) task.getxxslice(mosek.soltype.itr, 0, self.numvar, sol) self.sol = sol self.check_sol(Aiq, biq, Aeq, beq) return (sol, solsta) else: return (None, solsta)
def solve(self, pp_cone): " \n solve Positive Polynomial Program \n\n Parameters\n ----------\n pp_cone : {'psd', 'sdd'}\n cone for positivity constraints\n \n Returns\n ----------\n sol : solution vector\n sta : status\n " Aeq = sp.coo_matrix((0, self.numvar)) Aiq = sp.coo_matrix((0, self.numvar)) beq = np.zeros(0) biq = np.zeros(0) for (Aop_dict, b, n, d, tp) in self.constraints: Amat = self.get_bmat(Aop_dict, count_monomials_leq(n, d)) if (tp == 'eq'): Aeq = sp.bmat([[Aeq], [Amat]]) beq = np.hstack([beq, b.mon_coefs(d)]) if (tp == 'iq'): Aiq = sp.bmat([[Aiq], [Amat]]) biq = np.hstack([biq, b.mon_coefs(d)]) numcon_iq = Aiq.shape[0] numcon_eq = Aeq.shape[0] env = mosek.Env() task = env.Task(0, 0) task.appendvars(self.numvar) task.putvarboundslice(0, self.numvar, ([mosek.boundkey.fr] * self.numvar), ([0.0] * self.numvar), ([0.0] * self.numvar)) task.putcslice(0, self.numvar, self.c) task.putobjsense(mosek.objsense.minimize) A = sp.bmat([[Aeq], [Aiq]]) task.appendcons((numcon_eq + numcon_iq)) task.putaijlist(A.row, A.col, A.data) task.putconboundslice(0, (numcon_eq + numcon_iq), (([mosek.boundkey.fx] * numcon_eq) + ([mosek.boundkey.up] * numcon_iq)), (list(beq) + ([0.0] * numcon_iq)), (list(beq) + list(biq))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): Asp = speye(self.varsize(varname), self.varpos(varname), self.numvar) if (pp_cone == 'psd'): add_psd_mosek(task, Asp, np.zeros(self.varsize(varname))) if (pp_cone == 'sdd'): add_sdd_mosek(task, Asp, np.zeros(self.varsize(varname))) for (Aop_dict, b_pol, n, d, tp) in self.constraints: if (tp == 'pp'): if (pp_cone == 'psd'): add_psd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) if (pp_cone == 'sdd'): add_sdd_mosek(task, self.get_bmat(Aop_dict, count_monomials_leq(n, d)), b_pol.mon_coefs(d), PTrans.eye(n, d).Acg()) print('optimizing...') t_start = time.clock() task.optimize() print('solved in {:.2f}s'.format((time.clock() - t_start))) solsta = task.getsolsta(mosek.soltype.itr) print(solsta) if (solsta == mosek.solsta.optimal): sol = ([0.0] * self.numvar) task.getxxslice(mosek.soltype.itr, 0, self.numvar, sol) self.sol = sol self.check_sol(Aiq, biq, Aeq, beq) return (sol, solsta) else: return (None, solsta)<|docstring|>solve Positive Polynomial Program Parameters ---------- pp_cone : {'psd', 'sdd'} cone for positivity constraints Returns ---------- sol : solution vector sta : status<|endoftext|>
59fb6f05480c1d0f45f00fc4332e938cf316992e9094173b4806b15a1fc6cc7f
def check_sol(self, Aiq, biq, Aeq, beq, tol=1e-06): '\n check solution against tolerance to see if constraints are met,\n prints warnings messages if violations above tol are found\n REMARK: currently does not check manually added pp constraints\n ' if ((Aiq.shape[0] > 0) and (min((biq - Aiq.dot(self.sol))) < (- tol))): print('warning, iq constraint violated by {:f}'.format(abs(min((biq - Aiq.dot(self.sol)))))) if ((Aeq.shape[0] > 0) and (max(np.abs((Aeq.dot(self.sol) - beq))) > tol)): print('warning, eq constraint violated by {:f}'.format(max(np.abs((Aeq.dot(self.sol) - beq))))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): a = self.varpos(varname) b = (a + self.varsize(varname)) mat = vec_to_mat(self.sol[a:b]) (v, _) = np.linalg.eig(mat) if (min(v) < (- tol)): print('warning, pp constraint violated by {:f}'.format(abs(min(v))))
check solution against tolerance to see if constraints are met, prints warnings messages if violations above tol are found REMARK: currently does not check manually added pp constraints
posipoly/ppp.py
check_sol
pettni/posipoly
1
python
def check_sol(self, Aiq, biq, Aeq, beq, tol=1e-06): '\n check solution against tolerance to see if constraints are met,\n prints warnings messages if violations above tol are found\n REMARK: currently does not check manually added pp constraints\n ' if ((Aiq.shape[0] > 0) and (min((biq - Aiq.dot(self.sol))) < (- tol))): print('warning, iq constraint violated by {:f}'.format(abs(min((biq - Aiq.dot(self.sol)))))) if ((Aeq.shape[0] > 0) and (max(np.abs((Aeq.dot(self.sol) - beq))) > tol)): print('warning, eq constraint violated by {:f}'.format(max(np.abs((Aeq.dot(self.sol) - beq))))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): a = self.varpos(varname) b = (a + self.varsize(varname)) mat = vec_to_mat(self.sol[a:b]) (v, _) = np.linalg.eig(mat) if (min(v) < (- tol)): print('warning, pp constraint violated by {:f}'.format(abs(min(v))))
def check_sol(self, Aiq, biq, Aeq, beq, tol=1e-06): '\n check solution against tolerance to see if constraints are met,\n prints warnings messages if violations above tol are found\n REMARK: currently does not check manually added pp constraints\n ' if ((Aiq.shape[0] > 0) and (min((biq - Aiq.dot(self.sol))) < (- tol))): print('warning, iq constraint violated by {:f}'.format(abs(min((biq - Aiq.dot(self.sol)))))) if ((Aeq.shape[0] > 0) and (max(np.abs((Aeq.dot(self.sol) - beq))) > tol)): print('warning, eq constraint violated by {:f}'.format(max(np.abs((Aeq.dot(self.sol) - beq))))) for varname in self.varnames: if (self.varinfo[varname][2] == 'pp'): a = self.varpos(varname) b = (a + self.varsize(varname)) mat = vec_to_mat(self.sol[a:b]) (v, _) = np.linalg.eig(mat) if (min(v) < (- tol)): print('warning, pp constraint violated by {:f}'.format(abs(min(v))))<|docstring|>check solution against tolerance to see if constraints are met, prints warnings messages if violations above tol are found REMARK: currently does not check manually added pp constraints<|endoftext|>
b8652e3ba4b9c994a2fd7b8adaf81925a7f2f769341c4bb8fc9bfa4cd7d27c9a
def get_poly(self, varname): 'return a Polynomial object from solution' if (self.sol is None): raise Exception('no solution stored') if (varname not in self.varnames): raise Exception('unknown variable {}'.format()) a = self.varpos(varname) b = (a + self.varsize(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'coef'): mon_coefs = self.sol[a:b] else: mon_coefs = PTrans.eye(n, d).Acg().dot(self.sol[a:b]) return Polynomial.from_mon_coefs(n, mon_coefs)
return a Polynomial object from solution
posipoly/ppp.py
get_poly
pettni/posipoly
1
python
def get_poly(self, varname): if (self.sol is None): raise Exception('no solution stored') if (varname not in self.varnames): raise Exception('unknown variable {}'.format()) a = self.varpos(varname) b = (a + self.varsize(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'coef'): mon_coefs = self.sol[a:b] else: mon_coefs = PTrans.eye(n, d).Acg().dot(self.sol[a:b]) return Polynomial.from_mon_coefs(n, mon_coefs)
def get_poly(self, varname): if (self.sol is None): raise Exception('no solution stored') if (varname not in self.varnames): raise Exception('unknown variable {}'.format()) a = self.varpos(varname) b = (a + self.varsize(varname)) n = self.varinfo[varname][0] d = self.varinfo[varname][1] if (self.varinfo[varname][2] == 'coef'): mon_coefs = self.sol[a:b] else: mon_coefs = PTrans.eye(n, d).Acg().dot(self.sol[a:b]) return Polynomial.from_mon_coefs(n, mon_coefs)<|docstring|>return a Polynomial object from solution<|endoftext|>
4b2282af96d846a00d89dff2fffc08526d85b854196e7f7465e55677cc8c936b
def get_versions(): '\n Return basic information on our software installation, and out installed versions of packages.\n ' d = {'host': get_system_info(), 'packages': get_package_info(required_packages), 'optional': get_optional_info()} return d
Return basic information on our software installation, and out installed versions of packages.
src/hangar/diagnostics/ecosystem.py
get_versions
dxenonb/hangar-py
0
python
def get_versions(): '\n \n ' d = {'host': get_system_info(), 'packages': get_package_info(required_packages), 'optional': get_optional_info()} return d
def get_versions(): '\n \n ' d = {'host': get_system_info(), 'packages': get_package_info(required_packages), 'optional': get_optional_info()} return d<|docstring|>Return basic information on our software installation, and out installed versions of packages.<|endoftext|>
040590345ef1eeb3664414c07d9754da9574998419c9021d74f27302915ad4b0
def get_package_info(pkgs): ' get package versions for the passed required & optional packages ' pversions = [] for (modname, ver_f) in pkgs: try: mod = importlib.import_module(modname) ver = ver_f(mod) pversions.append((modname, ver)) except Exception: pversions.append((modname, None)) return pversions
get package versions for the passed required & optional packages
src/hangar/diagnostics/ecosystem.py
get_package_info
dxenonb/hangar-py
0
python
def get_package_info(pkgs): ' ' pversions = [] for (modname, ver_f) in pkgs: try: mod = importlib.import_module(modname) ver = ver_f(mod) pversions.append((modname, ver)) except Exception: pversions.append((modname, None)) return pversions
def get_package_info(pkgs): ' ' pversions = [] for (modname, ver_f) in pkgs: try: mod = importlib.import_module(modname) ver = ver_f(mod) pversions.append((modname, ver)) except Exception: pversions.append((modname, None)) return pversions<|docstring|>get package versions for the passed required & optional packages<|endoftext|>
3cfd1ce127c5aa45beb28a8527f8ca5285e695d74b08ea3bf78d5efeb316589d
def process_data(params): 'Provides end-to-end signal processing, including reading, detrending,\n tapering, filtering, instrument response removal, and saving.\n\n Parameters\n ----------\n params : SignalProcessing object\n Object containing signal processing parameters passed.\n\n Returns\n -------\n int\n Number of files saved to disk.\n ' st = processing.pipeline(params) if (st == 0): return 0 if (params.mode == 'preprocess'): if params.verbose: print('Trimming.') st.trim(starttime=obspy.core.UTCDateTime(params.start), endtime=obspy.core.UTCDateTime(params.stop)) count = 0 for tr in st: path = f'{params.writepath}/MSEED/{tr.stats.network}/{tr.stats.station}' if (not os.path.exists(path)): os.makedirs(path) fname = f'{tr.stats.network}.{tr.stats.station}.{tr.stats.channel}.{params.start.year}.{params.start.dayofyear:03d}.mseed' tr.data = tr.data.astype('float32') tr.write(f'{path}/{fname}', format='MSEED', encoding=4) count += 1 return count else: count = 0 if params.verbose: print('Running detector.') path = params.writepath if (not os.path.exists(path)): os.makedirs(path) for tr in st: fs = tr.stats.sampling_rate catalogue = pd.DataFrame(columns=['network', 'station', 'channel', 'dt_on', 'dt_off', 'dt_peak', 'peak', 'unit', 'fs', 'delta', 'npts', 'STA', 'LTA', 'on', 'off']) if (not os.path.exists(f'{path}/catalogue.csv')): catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False) secs = np.arange(0, (tr.stats.npts * tr.stats.delta), tr.stats.delta) time = (params.start_processing + pd.to_timedelta(secs, unit='sec')) if params.verbose: print('Calculating CFT.') if (params.detector == 'classic'): cft = trigger.classic_sta_lta(tr.data, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'recursive'): cft = trigger.recursive_sta_lta(tr, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'z'): cft = trigger.z_detect(tr.data, int((fs * 3))) if params.verbose: print('Locating triggers.') on_off = trigger.trigger_onset(cft, params.on, params.off) if isinstance(on_off, list): del catalogue continue on_off = on_off[(((time[on_off[(:, 0)]] >= params.start) & (time[on_off[(:, 0)]] < params.stop)), :)] nrows = on_off.shape[0] catalogue['network'] = [tr.stats.network for i in range(nrows)] catalogue['station'] = [tr.stats.station for i in range(nrows)] catalogue['channel'] = [tr.stats.channel for i in range(nrows)] catalogue['dt_on'] = time[on_off[(:, 0)]] catalogue['dt_off'] = time[on_off[(:, 1)]] i_max = [(on_off[(i, 0)] + np.argmax(abs(tr.data[on_off[(i, 0)]:on_off[(i, 1)]]))) for i in range(on_off.shape[0])] catalogue['dt_peak'] = time[i_max] catalogue['peak'] = tr.data[i_max] catalogue['unit'] = [params.output for i in range(nrows)] catalogue['fs'] = [fs for i in range(nrows)] catalogue['delta'] = [tr.stats.delta for i in range(nrows)] catalogue['npts'] = [tr.stats.npts for i in range(nrows)] catalogue['STA'] = [params.STA for i in range(nrows)] catalogue['LTA'] = [params.LTA for i in range(nrows)] catalogue['on'] = [params.on for i in range(nrows)] catalogue['off'] = [params.off for i in range(nrows)] catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False, header=False) if params.verbose: print('Catalogue built.') del catalogue count += on_off.shape[0] return count
Provides end-to-end signal processing, including reading, detrending, tapering, filtering, instrument response removal, and saving. Parameters ---------- params : SignalProcessing object Object containing signal processing parameters passed. Returns ------- int Number of files saved to disk.
RISProcess/workflows.py
process_data
firstkingofrome/RISProcess
0
python
def process_data(params): 'Provides end-to-end signal processing, including reading, detrending,\n tapering, filtering, instrument response removal, and saving.\n\n Parameters\n ----------\n params : SignalProcessing object\n Object containing signal processing parameters passed.\n\n Returns\n -------\n int\n Number of files saved to disk.\n ' st = processing.pipeline(params) if (st == 0): return 0 if (params.mode == 'preprocess'): if params.verbose: print('Trimming.') st.trim(starttime=obspy.core.UTCDateTime(params.start), endtime=obspy.core.UTCDateTime(params.stop)) count = 0 for tr in st: path = f'{params.writepath}/MSEED/{tr.stats.network}/{tr.stats.station}' if (not os.path.exists(path)): os.makedirs(path) fname = f'{tr.stats.network}.{tr.stats.station}.{tr.stats.channel}.{params.start.year}.{params.start.dayofyear:03d}.mseed' tr.data = tr.data.astype('float32') tr.write(f'{path}/{fname}', format='MSEED', encoding=4) count += 1 return count else: count = 0 if params.verbose: print('Running detector.') path = params.writepath if (not os.path.exists(path)): os.makedirs(path) for tr in st: fs = tr.stats.sampling_rate catalogue = pd.DataFrame(columns=['network', 'station', 'channel', 'dt_on', 'dt_off', 'dt_peak', 'peak', 'unit', 'fs', 'delta', 'npts', 'STA', 'LTA', 'on', 'off']) if (not os.path.exists(f'{path}/catalogue.csv')): catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False) secs = np.arange(0, (tr.stats.npts * tr.stats.delta), tr.stats.delta) time = (params.start_processing + pd.to_timedelta(secs, unit='sec')) if params.verbose: print('Calculating CFT.') if (params.detector == 'classic'): cft = trigger.classic_sta_lta(tr.data, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'recursive'): cft = trigger.recursive_sta_lta(tr, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'z'): cft = trigger.z_detect(tr.data, int((fs * 3))) if params.verbose: print('Locating triggers.') on_off = trigger.trigger_onset(cft, params.on, params.off) if isinstance(on_off, list): del catalogue continue on_off = on_off[(((time[on_off[(:, 0)]] >= params.start) & (time[on_off[(:, 0)]] < params.stop)), :)] nrows = on_off.shape[0] catalogue['network'] = [tr.stats.network for i in range(nrows)] catalogue['station'] = [tr.stats.station for i in range(nrows)] catalogue['channel'] = [tr.stats.channel for i in range(nrows)] catalogue['dt_on'] = time[on_off[(:, 0)]] catalogue['dt_off'] = time[on_off[(:, 1)]] i_max = [(on_off[(i, 0)] + np.argmax(abs(tr.data[on_off[(i, 0)]:on_off[(i, 1)]]))) for i in range(on_off.shape[0])] catalogue['dt_peak'] = time[i_max] catalogue['peak'] = tr.data[i_max] catalogue['unit'] = [params.output for i in range(nrows)] catalogue['fs'] = [fs for i in range(nrows)] catalogue['delta'] = [tr.stats.delta for i in range(nrows)] catalogue['npts'] = [tr.stats.npts for i in range(nrows)] catalogue['STA'] = [params.STA for i in range(nrows)] catalogue['LTA'] = [params.LTA for i in range(nrows)] catalogue['on'] = [params.on for i in range(nrows)] catalogue['off'] = [params.off for i in range(nrows)] catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False, header=False) if params.verbose: print('Catalogue built.') del catalogue count += on_off.shape[0] return count
def process_data(params): 'Provides end-to-end signal processing, including reading, detrending,\n tapering, filtering, instrument response removal, and saving.\n\n Parameters\n ----------\n params : SignalProcessing object\n Object containing signal processing parameters passed.\n\n Returns\n -------\n int\n Number of files saved to disk.\n ' st = processing.pipeline(params) if (st == 0): return 0 if (params.mode == 'preprocess'): if params.verbose: print('Trimming.') st.trim(starttime=obspy.core.UTCDateTime(params.start), endtime=obspy.core.UTCDateTime(params.stop)) count = 0 for tr in st: path = f'{params.writepath}/MSEED/{tr.stats.network}/{tr.stats.station}' if (not os.path.exists(path)): os.makedirs(path) fname = f'{tr.stats.network}.{tr.stats.station}.{tr.stats.channel}.{params.start.year}.{params.start.dayofyear:03d}.mseed' tr.data = tr.data.astype('float32') tr.write(f'{path}/{fname}', format='MSEED', encoding=4) count += 1 return count else: count = 0 if params.verbose: print('Running detector.') path = params.writepath if (not os.path.exists(path)): os.makedirs(path) for tr in st: fs = tr.stats.sampling_rate catalogue = pd.DataFrame(columns=['network', 'station', 'channel', 'dt_on', 'dt_off', 'dt_peak', 'peak', 'unit', 'fs', 'delta', 'npts', 'STA', 'LTA', 'on', 'off']) if (not os.path.exists(f'{path}/catalogue.csv')): catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False) secs = np.arange(0, (tr.stats.npts * tr.stats.delta), tr.stats.delta) time = (params.start_processing + pd.to_timedelta(secs, unit='sec')) if params.verbose: print('Calculating CFT.') if (params.detector == 'classic'): cft = trigger.classic_sta_lta(tr.data, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'recursive'): cft = trigger.recursive_sta_lta(tr, int((fs * params.STA)), int((fs * params.LTA))) elif (params.detector == 'z'): cft = trigger.z_detect(tr.data, int((fs * 3))) if params.verbose: print('Locating triggers.') on_off = trigger.trigger_onset(cft, params.on, params.off) if isinstance(on_off, list): del catalogue continue on_off = on_off[(((time[on_off[(:, 0)]] >= params.start) & (time[on_off[(:, 0)]] < params.stop)), :)] nrows = on_off.shape[0] catalogue['network'] = [tr.stats.network for i in range(nrows)] catalogue['station'] = [tr.stats.station for i in range(nrows)] catalogue['channel'] = [tr.stats.channel for i in range(nrows)] catalogue['dt_on'] = time[on_off[(:, 0)]] catalogue['dt_off'] = time[on_off[(:, 1)]] i_max = [(on_off[(i, 0)] + np.argmax(abs(tr.data[on_off[(i, 0)]:on_off[(i, 1)]]))) for i in range(on_off.shape[0])] catalogue['dt_peak'] = time[i_max] catalogue['peak'] = tr.data[i_max] catalogue['unit'] = [params.output for i in range(nrows)] catalogue['fs'] = [fs for i in range(nrows)] catalogue['delta'] = [tr.stats.delta for i in range(nrows)] catalogue['npts'] = [tr.stats.npts for i in range(nrows)] catalogue['STA'] = [params.STA for i in range(nrows)] catalogue['LTA'] = [params.LTA for i in range(nrows)] catalogue['on'] = [params.on for i in range(nrows)] catalogue['off'] = [params.off for i in range(nrows)] catalogue.to_csv(f'{path}/catalogue.csv', mode='a', index=False, header=False) if params.verbose: print('Catalogue built.') del catalogue count += on_off.shape[0] return count<|docstring|>Provides end-to-end signal processing, including reading, detrending, tapering, filtering, instrument response removal, and saving. Parameters ---------- params : SignalProcessing object Object containing signal processing parameters passed. Returns ------- int Number of files saved to disk.<|endoftext|>
69a6cdb15124c9b081cfedda0bdab643ab5731261bf55b381ba31370f1ea79e8
def to_json_serializable(data): 'Make an object containing numpy arrays/scalars JSON serializable.' if (data is None): return None if isinstance(data, dict): return {key: to_json_serializable(value) for (key, value) in data.items()} elif (isinstance(data, list) or isinstance(data, tuple)): return [to_json_serializable(v) for v in data] elif isinstance(data, np.ndarray): return data.tolist() elif np.isscalar(data): return data else: raise ValueError('Unsupported JSON type `{}` (key `{}`)'.format(type(data), data))
Make an object containing numpy arrays/scalars JSON serializable.
shepherd/runner/json_runner.py
to_json_serializable
iterait/shepherd
5
python
def to_json_serializable(data): if (data is None): return None if isinstance(data, dict): return {key: to_json_serializable(value) for (key, value) in data.items()} elif (isinstance(data, list) or isinstance(data, tuple)): return [to_json_serializable(v) for v in data] elif isinstance(data, np.ndarray): return data.tolist() elif np.isscalar(data): return data else: raise ValueError('Unsupported JSON type `{}` (key `{}`)'.format(type(data), data))
def to_json_serializable(data): if (data is None): return None if isinstance(data, dict): return {key: to_json_serializable(value) for (key, value) in data.items()} elif (isinstance(data, list) or isinstance(data, tuple)): return [to_json_serializable(v) for v in data] elif isinstance(data, np.ndarray): return data.tolist() elif np.isscalar(data): return data else: raise ValueError('Unsupported JSON type `{}` (key `{}`)'.format(type(data), data))<|docstring|>Make an object containing numpy arrays/scalars JSON serializable.<|endoftext|>
9da664b5c774a97a988de72029ace0500f6ea8fc1ad2fbcb06d5d477d90b37f8
def run(model: el.AbstractModel, dataset: el.AbstractDataset, stream_name: str, payload: Any) -> el.Batch: '\n Get the specified data stream from the given dataset, apply the given model on its batches and return the results.\n\n The components have to be **emloop** compatible with:\n - dataset having method named ``[stream_name]_stream`` taking the payload and returning the stream\n - (optional) dataset having method named ``postprocess_batch`` taking both the input and output batches and\n returning the post-processed batch\n\n :param model: emloop model to be run\n :param dataset: emloop dataset to get the stream from\n :param stream_name: stream name\n :param payload: payload passed to the method creating the stream\n :return: result batch (if the stream produces multiple batches its the concatenation of all the results)\n ' result = defaultdict(list) for input_batch in getattr(dataset, (stream_name + '_stream'))(payload): logging.info('Another batch (%s)', list(input_batch.keys())) output_batch = model.run(input_batch, train=False, stream=None) if hasattr(dataset, 'postprocess_batch'): logging.info('\tPostprocessing') result_batch = dataset.postprocess_batch(input_batch=input_batch, output_batch=output_batch) logging.info('\tdone') else: logging.info('Skipping postprocessing') result_batch = output_batch for (source, value) in result_batch.items(): result[source] += list(value) return result
Get the specified data stream from the given dataset, apply the given model on its batches and return the results. The components have to be **emloop** compatible with: - dataset having method named ``[stream_name]_stream`` taking the payload and returning the stream - (optional) dataset having method named ``postprocess_batch`` taking both the input and output batches and returning the post-processed batch :param model: emloop model to be run :param dataset: emloop dataset to get the stream from :param stream_name: stream name :param payload: payload passed to the method creating the stream :return: result batch (if the stream produces multiple batches its the concatenation of all the results)
shepherd/runner/json_runner.py
run
iterait/shepherd
5
python
def run(model: el.AbstractModel, dataset: el.AbstractDataset, stream_name: str, payload: Any) -> el.Batch: '\n Get the specified data stream from the given dataset, apply the given model on its batches and return the results.\n\n The components have to be **emloop** compatible with:\n - dataset having method named ``[stream_name]_stream`` taking the payload and returning the stream\n - (optional) dataset having method named ``postprocess_batch`` taking both the input and output batches and\n returning the post-processed batch\n\n :param model: emloop model to be run\n :param dataset: emloop dataset to get the stream from\n :param stream_name: stream name\n :param payload: payload passed to the method creating the stream\n :return: result batch (if the stream produces multiple batches its the concatenation of all the results)\n ' result = defaultdict(list) for input_batch in getattr(dataset, (stream_name + '_stream'))(payload): logging.info('Another batch (%s)', list(input_batch.keys())) output_batch = model.run(input_batch, train=False, stream=None) if hasattr(dataset, 'postprocess_batch'): logging.info('\tPostprocessing') result_batch = dataset.postprocess_batch(input_batch=input_batch, output_batch=output_batch) logging.info('\tdone') else: logging.info('Skipping postprocessing') result_batch = output_batch for (source, value) in result_batch.items(): result[source] += list(value) return result
def run(model: el.AbstractModel, dataset: el.AbstractDataset, stream_name: str, payload: Any) -> el.Batch: '\n Get the specified data stream from the given dataset, apply the given model on its batches and return the results.\n\n The components have to be **emloop** compatible with:\n - dataset having method named ``[stream_name]_stream`` taking the payload and returning the stream\n - (optional) dataset having method named ``postprocess_batch`` taking both the input and output batches and\n returning the post-processed batch\n\n :param model: emloop model to be run\n :param dataset: emloop dataset to get the stream from\n :param stream_name: stream name\n :param payload: payload passed to the method creating the stream\n :return: result batch (if the stream produces multiple batches its the concatenation of all the results)\n ' result = defaultdict(list) for input_batch in getattr(dataset, (stream_name + '_stream'))(payload): logging.info('Another batch (%s)', list(input_batch.keys())) output_batch = model.run(input_batch, train=False, stream=None) if hasattr(dataset, 'postprocess_batch'): logging.info('\tPostprocessing') result_batch = dataset.postprocess_batch(input_batch=input_batch, output_batch=output_batch) logging.info('\tdone') else: logging.info('Skipping postprocessing') result_batch = output_batch for (source, value) in result_batch.items(): result[source] += list(value) return result<|docstring|>Get the specified data stream from the given dataset, apply the given model on its batches and return the results. The components have to be **emloop** compatible with: - dataset having method named ``[stream_name]_stream`` taking the payload and returning the stream - (optional) dataset having method named ``postprocess_batch`` taking both the input and output batches and returning the post-processed batch :param model: emloop model to be run :param dataset: emloop dataset to get the stream from :param stream_name: stream name :param payload: payload passed to the method creating the stream :return: result batch (if the stream produces multiple batches its the concatenation of all the results)<|endoftext|>
97feba130cace2c037110d86d2acf5ea25eec9429baed5cdd72fc23232626ec6
def _process_job(self, input_path: str, output_path: str) -> None: '\n Process a JSON job\n - load ``input_path``/``input``\n - create dataset stream with the loaded JSON\n - run the model\n - save the output to ``output_path``/``output``\n\n :param input_path: input data directory\n :param output_path: output data directory\n ' self._load_dataset() self._load_model() payload = json.load(open(path.join(input_path, DEFAULT_PAYLOAD_FILE))) result = run(self._model, self._dataset, self._stream_name, payload) result_json = to_json_serializable(result) json.dump(result_json, open(path.join(output_path, DEFAULT_OUTPUT_FILE), 'w'))
Process a JSON job - load ``input_path``/``input`` - create dataset stream with the loaded JSON - run the model - save the output to ``output_path``/``output`` :param input_path: input data directory :param output_path: output data directory
shepherd/runner/json_runner.py
_process_job
iterait/shepherd
5
python
def _process_job(self, input_path: str, output_path: str) -> None: '\n Process a JSON job\n - load ``input_path``/``input``\n - create dataset stream with the loaded JSON\n - run the model\n - save the output to ``output_path``/``output``\n\n :param input_path: input data directory\n :param output_path: output data directory\n ' self._load_dataset() self._load_model() payload = json.load(open(path.join(input_path, DEFAULT_PAYLOAD_FILE))) result = run(self._model, self._dataset, self._stream_name, payload) result_json = to_json_serializable(result) json.dump(result_json, open(path.join(output_path, DEFAULT_OUTPUT_FILE), 'w'))
def _process_job(self, input_path: str, output_path: str) -> None: '\n Process a JSON job\n - load ``input_path``/``input``\n - create dataset stream with the loaded JSON\n - run the model\n - save the output to ``output_path``/``output``\n\n :param input_path: input data directory\n :param output_path: output data directory\n ' self._load_dataset() self._load_model() payload = json.load(open(path.join(input_path, DEFAULT_PAYLOAD_FILE))) result = run(self._model, self._dataset, self._stream_name, payload) result_json = to_json_serializable(result) json.dump(result_json, open(path.join(output_path, DEFAULT_OUTPUT_FILE), 'w'))<|docstring|>Process a JSON job - load ``input_path``/``input`` - create dataset stream with the loaded JSON - run the model - save the output to ``output_path``/``output`` :param input_path: input data directory :param output_path: output data directory<|endoftext|>
21df66eb71da9371128ed988b1ca7b2c76ff611bb653315912d53b7bccd0e74c
def repackage_hidden(h): 'Wraps hidden states in new Tensors, to detach them from their history.' if isinstance(h, torch.Tensor): return h.detach() else: return tuple((repackage_hidden(v) for v in h))
Wraps hidden states in new Tensors, to detach them from their history.
telemanom/modeling.py
repackage_hidden
sedgewickmm18/telemanom
0
python
def repackage_hidden(h): if isinstance(h, torch.Tensor): return h.detach() else: return tuple((repackage_hidden(v) for v in h))
def repackage_hidden(h): if isinstance(h, torch.Tensor): return h.detach() else: return tuple((repackage_hidden(v) for v in h))<|docstring|>Wraps hidden states in new Tensors, to detach them from their history.<|endoftext|>
edf8e2edc8deb9823ac22596955909ccf8bf58ea4ed730a8cdfc59879b1768c0
def __init__(self, config, run_id, channel, Path=None, Train=True): '\n Loads/trains RNN and predicts future telemetry values for a channel.\n\n Args:\n config (obj): Config object containing parameters for processing\n and model training\n run_id (str): Datetime referencing set of predictions in use\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Attributes:\n config (obj): see Args\n chan_id (str): channel id\n run_id (str): see Args\n y_hat (arr): predicted channel values\n model (obj): trained RNN model for predicting channel values\n ' self.name = 'Model' self.config = config self.chan_id = channel.id self.run_id = run_id self.y_hat = np.array([]) self.model = None self.onnx_session = None self.history = None if (Path is None): Path = '' if (not Train): self.new_model((None, channel.X_train.shape[2])) elif (not self.config.train): try: self.load(Path) except FileNotFoundError: path = os.path.join(Path, 'data', self.config.use_id, 'models', (self.chan_id + '.h5')) logger.warning("Training new model, couldn't find existing model at {}".format(path)) self.train_new(channel) self.save(Path) else: self.train_new(channel) self.save(Path)
Loads/trains RNN and predicts future telemetry values for a channel. Args: config (obj): Config object containing parameters for processing and model training run_id (str): Datetime referencing set of predictions in use channel (obj): Channel class object containing train/test data for X,y for a single channel Attributes: config (obj): see Args chan_id (str): channel id run_id (str): see Args y_hat (arr): predicted channel values model (obj): trained RNN model for predicting channel values
telemanom/modeling.py
__init__
sedgewickmm18/telemanom
0
python
def __init__(self, config, run_id, channel, Path=None, Train=True): '\n Loads/trains RNN and predicts future telemetry values for a channel.\n\n Args:\n config (obj): Config object containing parameters for processing\n and model training\n run_id (str): Datetime referencing set of predictions in use\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Attributes:\n config (obj): see Args\n chan_id (str): channel id\n run_id (str): see Args\n y_hat (arr): predicted channel values\n model (obj): trained RNN model for predicting channel values\n ' self.name = 'Model' self.config = config self.chan_id = channel.id self.run_id = run_id self.y_hat = np.array([]) self.model = None self.onnx_session = None self.history = None if (Path is None): Path = if (not Train): self.new_model((None, channel.X_train.shape[2])) elif (not self.config.train): try: self.load(Path) except FileNotFoundError: path = os.path.join(Path, 'data', self.config.use_id, 'models', (self.chan_id + '.h5')) logger.warning("Training new model, couldn't find existing model at {}".format(path)) self.train_new(channel) self.save(Path) else: self.train_new(channel) self.save(Path)
def __init__(self, config, run_id, channel, Path=None, Train=True): '\n Loads/trains RNN and predicts future telemetry values for a channel.\n\n Args:\n config (obj): Config object containing parameters for processing\n and model training\n run_id (str): Datetime referencing set of predictions in use\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Attributes:\n config (obj): see Args\n chan_id (str): channel id\n run_id (str): see Args\n y_hat (arr): predicted channel values\n model (obj): trained RNN model for predicting channel values\n ' self.name = 'Model' self.config = config self.chan_id = channel.id self.run_id = run_id self.y_hat = np.array([]) self.model = None self.onnx_session = None self.history = None if (Path is None): Path = if (not Train): self.new_model((None, channel.X_train.shape[2])) elif (not self.config.train): try: self.load(Path) except FileNotFoundError: path = os.path.join(Path, 'data', self.config.use_id, 'models', (self.chan_id + '.h5')) logger.warning("Training new model, couldn't find existing model at {}".format(path)) self.train_new(channel) self.save(Path) else: self.train_new(channel) self.save(Path)<|docstring|>Loads/trains RNN and predicts future telemetry values for a channel. Args: config (obj): Config object containing parameters for processing and model training run_id (str): Datetime referencing set of predictions in use channel (obj): Channel class object containing train/test data for X,y for a single channel Attributes: config (obj): see Args chan_id (str): channel id run_id (str): see Args y_hat (arr): predicted channel values model (obj): trained RNN model for predicting channel values<|endoftext|>
26cdb7311e4d4801f26d1723f7ba41003755d5a7275e15b4a1fc5b0e89fafb01
def load(self, Path=None): '\n Load model for channel.\n ' logger.info('Loading pre-trained model') self.model = self.model.load_state_dict(torch.load(os.path.join(Path, self.config.use_id, 'models', (self.chan_id + '.h5'))))
Load model for channel.
telemanom/modeling.py
load
sedgewickmm18/telemanom
0
python
def load(self, Path=None): '\n \n ' logger.info('Loading pre-trained model') self.model = self.model.load_state_dict(torch.load(os.path.join(Path, self.config.use_id, 'models', (self.chan_id + '.h5'))))
def load(self, Path=None): '\n \n ' logger.info('Loading pre-trained model') self.model = self.model.load_state_dict(torch.load(os.path.join(Path, self.config.use_id, 'models', (self.chan_id + '.h5'))))<|docstring|>Load model for channel.<|endoftext|>
6c29c56cbfaf032788d2669d2d673244d45ed1fa1820f68402aa2c0217d4379c
def load_onnx(self, Path=None): '\n Load ONNX model\n ' import onnxruntime if (Path is None): Path = '' self.onnx_session = onnxruntime.InferenceSession(Path)
Load ONNX model
telemanom/modeling.py
load_onnx
sedgewickmm18/telemanom
0
python
def load_onnx(self, Path=None): '\n \n ' import onnxruntime if (Path is None): Path = self.onnx_session = onnxruntime.InferenceSession(Path)
def load_onnx(self, Path=None): '\n \n ' import onnxruntime if (Path is None): Path = self.onnx_session = onnxruntime.InferenceSession(Path)<|docstring|>Load ONNX model<|endoftext|>
30c01d4bba4bf344474db1b16951bae8d6aba2c4756c36cb062d8791a5ea5af8
def new_model(self, Input_shape): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' if (self.model is not None): return self.model = LSTM_2L(n_features=Input_shape[1], hidden_dims=self.config.layers, seq_length=self.config.l_s, batch_size=self.config.lstm_batch_size, n_predictions=self.config.n_predictions, dropout=self.config.dropout) print('input shape: ', Input_shape) return
Train LSTM model according to specifications in config.yaml. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel
telemanom/modeling.py
new_model
sedgewickmm18/telemanom
0
python
def new_model(self, Input_shape): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' if (self.model is not None): return self.model = LSTM_2L(n_features=Input_shape[1], hidden_dims=self.config.layers, seq_length=self.config.l_s, batch_size=self.config.lstm_batch_size, n_predictions=self.config.n_predictions, dropout=self.config.dropout) print('input shape: ', Input_shape) return
def new_model(self, Input_shape): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' if (self.model is not None): return self.model = LSTM_2L(n_features=Input_shape[1], hidden_dims=self.config.layers, seq_length=self.config.l_s, batch_size=self.config.lstm_batch_size, n_predictions=self.config.n_predictions, dropout=self.config.dropout) print('input shape: ', Input_shape) return<|docstring|>Train LSTM model according to specifications in config.yaml. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel<|endoftext|>
4e40e3ebf66f8e2c067f7dfd59e6a6b4b604cefccc892843559688e812b134ec
def train_new(self, channel): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' self.new_model((None, channel.X_train.shape[2])) self.model.apply(initialize_weights) training_losses = [] validation_losses = [] loss_function = nn.MSELoss() optimizer = torch.optim.Adam(self.model.parameters()) train_hist = np.zeros(self.config.epochs) (X_train, X_validation, y_train, y_validation) = train_test_split(channel.X_train, channel.y_train, train_size=0.8) print('Shapes: ', channel.X_train.shape, channel.y_train.shape) print('Training shapes: ', X_train.shape, y_train.shape) print('Validation shapes: ', X_validation.shape, y_validation.shape) train_dataset = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train)) validation_dataset = TensorDataset(torch.Tensor(X_validation), torch.Tensor(y_validation)) train_loader = DataLoader(dataset=train_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) val_loader = DataLoader(dataset=validation_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) self.model.train() print('Beginning model training...') hidden = self.model.init_hidden_state() for t in range(self.config.epochs): train_losses_batch = [] print('Epoch ', t) i = 0 for (X_batch_train, y_batch_train) in train_loader: print('Batch ', i) i += 1 hidden = repackage_hidden(hidden) (y_hat_train, hidden) = self.model(X_batch_train, hidden=hidden) loss = loss_function(y_hat_train.float(), y_batch_train) train_loss_batch = loss.item() loss.backward() optimizer.step() optimizer.zero_grad() train_losses_batch.append(train_loss_batch) training_loss = np.mean(train_losses_batch) print('After batch ', (i - 1), training_loss) training_losses.append(training_loss) with torch.no_grad(): val_losses_batch = [] for (X_val_batch, y_val_batch) in val_loader: self.model.eval() (y_hat_val, _) = self.model(X_val_batch, hidden=hidden) val_loss_batch = loss_function(y_hat_val.float(), y_val_batch).item() val_losses_batch.append(val_loss_batch) validation_loss = np.mean(val_losses_batch) validation_losses.append(validation_loss) print(f'[{(t + 1)}] Training loss: {training_loss} Validation loss: {validation_loss} ') if ((training_loss < 0.02) and (validation_loss < 0.02)): break print('Training complete...') return self.model.eval()
Train LSTM model according to specifications in config.yaml. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel
telemanom/modeling.py
train_new
sedgewickmm18/telemanom
0
python
def train_new(self, channel): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' self.new_model((None, channel.X_train.shape[2])) self.model.apply(initialize_weights) training_losses = [] validation_losses = [] loss_function = nn.MSELoss() optimizer = torch.optim.Adam(self.model.parameters()) train_hist = np.zeros(self.config.epochs) (X_train, X_validation, y_train, y_validation) = train_test_split(channel.X_train, channel.y_train, train_size=0.8) print('Shapes: ', channel.X_train.shape, channel.y_train.shape) print('Training shapes: ', X_train.shape, y_train.shape) print('Validation shapes: ', X_validation.shape, y_validation.shape) train_dataset = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train)) validation_dataset = TensorDataset(torch.Tensor(X_validation), torch.Tensor(y_validation)) train_loader = DataLoader(dataset=train_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) val_loader = DataLoader(dataset=validation_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) self.model.train() print('Beginning model training...') hidden = self.model.init_hidden_state() for t in range(self.config.epochs): train_losses_batch = [] print('Epoch ', t) i = 0 for (X_batch_train, y_batch_train) in train_loader: print('Batch ', i) i += 1 hidden = repackage_hidden(hidden) (y_hat_train, hidden) = self.model(X_batch_train, hidden=hidden) loss = loss_function(y_hat_train.float(), y_batch_train) train_loss_batch = loss.item() loss.backward() optimizer.step() optimizer.zero_grad() train_losses_batch.append(train_loss_batch) training_loss = np.mean(train_losses_batch) print('After batch ', (i - 1), training_loss) training_losses.append(training_loss) with torch.no_grad(): val_losses_batch = [] for (X_val_batch, y_val_batch) in val_loader: self.model.eval() (y_hat_val, _) = self.model(X_val_batch, hidden=hidden) val_loss_batch = loss_function(y_hat_val.float(), y_val_batch).item() val_losses_batch.append(val_loss_batch) validation_loss = np.mean(val_losses_batch) validation_losses.append(validation_loss) print(f'[{(t + 1)}] Training loss: {training_loss} Validation loss: {validation_loss} ') if ((training_loss < 0.02) and (validation_loss < 0.02)): break print('Training complete...') return self.model.eval()
def train_new(self, channel): '\n Train LSTM model according to specifications in config.yaml.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n ' self.new_model((None, channel.X_train.shape[2])) self.model.apply(initialize_weights) training_losses = [] validation_losses = [] loss_function = nn.MSELoss() optimizer = torch.optim.Adam(self.model.parameters()) train_hist = np.zeros(self.config.epochs) (X_train, X_validation, y_train, y_validation) = train_test_split(channel.X_train, channel.y_train, train_size=0.8) print('Shapes: ', channel.X_train.shape, channel.y_train.shape) print('Training shapes: ', X_train.shape, y_train.shape) print('Validation shapes: ', X_validation.shape, y_validation.shape) train_dataset = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train)) validation_dataset = TensorDataset(torch.Tensor(X_validation), torch.Tensor(y_validation)) train_loader = DataLoader(dataset=train_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) val_loader = DataLoader(dataset=validation_dataset, batch_size=self.config.lstm_batch_size, drop_last=True, shuffle=True) self.model.train() print('Beginning model training...') hidden = self.model.init_hidden_state() for t in range(self.config.epochs): train_losses_batch = [] print('Epoch ', t) i = 0 for (X_batch_train, y_batch_train) in train_loader: print('Batch ', i) i += 1 hidden = repackage_hidden(hidden) (y_hat_train, hidden) = self.model(X_batch_train, hidden=hidden) loss = loss_function(y_hat_train.float(), y_batch_train) train_loss_batch = loss.item() loss.backward() optimizer.step() optimizer.zero_grad() train_losses_batch.append(train_loss_batch) training_loss = np.mean(train_losses_batch) print('After batch ', (i - 1), training_loss) training_losses.append(training_loss) with torch.no_grad(): val_losses_batch = [] for (X_val_batch, y_val_batch) in val_loader: self.model.eval() (y_hat_val, _) = self.model(X_val_batch, hidden=hidden) val_loss_batch = loss_function(y_hat_val.float(), y_val_batch).item() val_losses_batch.append(val_loss_batch) validation_loss = np.mean(val_losses_batch) validation_losses.append(validation_loss) print(f'[{(t + 1)}] Training loss: {training_loss} Validation loss: {validation_loss} ') if ((training_loss < 0.02) and (validation_loss < 0.02)): break print('Training complete...') return self.model.eval()<|docstring|>Train LSTM model according to specifications in config.yaml. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel<|endoftext|>
552b35655e82d7d5d1187c18176e2d0702f68fe718a68de0158ce383d71ac559
def export(self, Path=None): '\n Export trained model as ONNX\n ' self.model.eval() if (Path is None): Path = '' torch_in = None torch_out = None with torch.no_grad(): torch_in = torch.randn(self.model.batch_size, self.model.hidden_dim, self.model.n_features, requires_grad=True) (torch_out, _) = self.model(torch_in) torch.onnx.export(self.model, torch_in, Path) "\n onnx_model = onnx.load(Path)\n\n logger.debug('Checked model: ', str(onnx_model.graph.input[0]))\n\n onnx.checker.check_model(onnx_model)\n "
Export trained model as ONNX
telemanom/modeling.py
export
sedgewickmm18/telemanom
0
python
def export(self, Path=None): '\n \n ' self.model.eval() if (Path is None): Path = torch_in = None torch_out = None with torch.no_grad(): torch_in = torch.randn(self.model.batch_size, self.model.hidden_dim, self.model.n_features, requires_grad=True) (torch_out, _) = self.model(torch_in) torch.onnx.export(self.model, torch_in, Path) "\n onnx_model = onnx.load(Path)\n\n logger.debug('Checked model: ', str(onnx_model.graph.input[0]))\n\n onnx.checker.check_model(onnx_model)\n "
def export(self, Path=None): '\n \n ' self.model.eval() if (Path is None): Path = torch_in = None torch_out = None with torch.no_grad(): torch_in = torch.randn(self.model.batch_size, self.model.hidden_dim, self.model.n_features, requires_grad=True) (torch_out, _) = self.model(torch_in) torch.onnx.export(self.model, torch_in, Path) "\n onnx_model = onnx.load(Path)\n\n logger.debug('Checked model: ', str(onnx_model.graph.input[0]))\n\n onnx.checker.check_model(onnx_model)\n "<|docstring|>Export trained model as ONNX<|endoftext|>
3fb612763cce0ec7b064fafc75f540b5f03ad837e9f46f5e745fe9fac442b5f9
def save(self, Path=None): '\n Save trained model.\n ' self.model.eval() if (Path is None): Path = '' torch.save(self.model.state_dict(), os.path.join(Path, 'data', self.run_id, 'models', '{}.torch'.format(self.chan_id)))
Save trained model.
telemanom/modeling.py
save
sedgewickmm18/telemanom
0
python
def save(self, Path=None): '\n \n ' self.model.eval() if (Path is None): Path = torch.save(self.model.state_dict(), os.path.join(Path, 'data', self.run_id, 'models', '{}.torch'.format(self.chan_id)))
def save(self, Path=None): '\n \n ' self.model.eval() if (Path is None): Path = torch.save(self.model.state_dict(), os.path.join(Path, 'data', self.run_id, 'models', '{}.torch'.format(self.chan_id)))<|docstring|>Save trained model.<|endoftext|>
c66a575629427c42929dbda0c506e51bb404adcda066eaec85fbf78586ac0e93
def aggregate_predictions(self, y_hat_batch, method='first'): '\n Aggregates predictions for each timestep. When predicting n steps\n ahead where n > 1, will end up with multiple predictions for a\n timestep.\n\n Args:\n y_hat_batch (arr): predictions shape (<batch length>, <n_preds)\n method (string): indicates how to aggregate for a timestep - "first"\n or "mean"\n ' agg_y_hat_batch = np.array([]) for t in range(len(y_hat_batch)): start_idx = (t - self.config.n_predictions) start_idx = (start_idx if (start_idx >= 0) else 0) y_hat_t = np.flipud(y_hat_batch[start_idx:(t + 1)]).diagonal() if (method == 'first'): agg_y_hat_batch = np.append(agg_y_hat_batch, [y_hat_t[0]]) elif (method == 'mean'): agg_y_hat_batch = np.append(agg_y_hat_batch, np.mean(y_hat_t)) agg_y_hat_batch = agg_y_hat_batch.reshape(len(agg_y_hat_batch), 1) self.y_hat = np.append(self.y_hat, agg_y_hat_batch)
Aggregates predictions for each timestep. When predicting n steps ahead where n > 1, will end up with multiple predictions for a timestep. Args: y_hat_batch (arr): predictions shape (<batch length>, <n_preds) method (string): indicates how to aggregate for a timestep - "first" or "mean"
telemanom/modeling.py
aggregate_predictions
sedgewickmm18/telemanom
0
python
def aggregate_predictions(self, y_hat_batch, method='first'): '\n Aggregates predictions for each timestep. When predicting n steps\n ahead where n > 1, will end up with multiple predictions for a\n timestep.\n\n Args:\n y_hat_batch (arr): predictions shape (<batch length>, <n_preds)\n method (string): indicates how to aggregate for a timestep - "first"\n or "mean"\n ' agg_y_hat_batch = np.array([]) for t in range(len(y_hat_batch)): start_idx = (t - self.config.n_predictions) start_idx = (start_idx if (start_idx >= 0) else 0) y_hat_t = np.flipud(y_hat_batch[start_idx:(t + 1)]).diagonal() if (method == 'first'): agg_y_hat_batch = np.append(agg_y_hat_batch, [y_hat_t[0]]) elif (method == 'mean'): agg_y_hat_batch = np.append(agg_y_hat_batch, np.mean(y_hat_t)) agg_y_hat_batch = agg_y_hat_batch.reshape(len(agg_y_hat_batch), 1) self.y_hat = np.append(self.y_hat, agg_y_hat_batch)
def aggregate_predictions(self, y_hat_batch, method='first'): '\n Aggregates predictions for each timestep. When predicting n steps\n ahead where n > 1, will end up with multiple predictions for a\n timestep.\n\n Args:\n y_hat_batch (arr): predictions shape (<batch length>, <n_preds)\n method (string): indicates how to aggregate for a timestep - "first"\n or "mean"\n ' agg_y_hat_batch = np.array([]) for t in range(len(y_hat_batch)): start_idx = (t - self.config.n_predictions) start_idx = (start_idx if (start_idx >= 0) else 0) y_hat_t = np.flipud(y_hat_batch[start_idx:(t + 1)]).diagonal() if (method == 'first'): agg_y_hat_batch = np.append(agg_y_hat_batch, [y_hat_t[0]]) elif (method == 'mean'): agg_y_hat_batch = np.append(agg_y_hat_batch, np.mean(y_hat_t)) agg_y_hat_batch = agg_y_hat_batch.reshape(len(agg_y_hat_batch), 1) self.y_hat = np.append(self.y_hat, agg_y_hat_batch)<|docstring|>Aggregates predictions for each timestep. When predicting n steps ahead where n > 1, will end up with multiple predictions for a timestep. Args: y_hat_batch (arr): predictions shape (<batch length>, <n_preds) method (string): indicates how to aggregate for a timestep - "first" or "mean"<|endoftext|>
d61e5d8d5e8039f70753999b6c3d4db5739979370f2b4abdec72ca5708db6080
def batch_predict(self, channel, Train=False, Path=None): '\n Used trained LSTM model to predict test data arriving in batches.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Returns:\n channel (obj): Channel class object with y_hat values as attribute\n ' if Train: num_batches = int(((channel.y_train.shape[0] - self.config.l_s) / self.config.batch_size)) else: num_batches = int(((channel.y_test.shape[0] - self.config.l_s) / self.config.batch_size)) logger.debug('predict: num_batches ', num_batches) if (num_batches < 0): raise ValueError('l_s ({}) too large for stream length {}.'.format(self.config.l_s, channel.y_test.shape[0])) for i in range(0, (num_batches + 1)): prior_idx = (i * self.config.batch_size) idx = ((i + 1) * self.config.batch_size) if ((i + 1) == (num_batches + 1)): if Train: idx = channel.y_test.shape[0] else: idx = channel.y_train.shape[0] if Train: X_train_batch = channel.X_train[prior_idx:idx] if (self.onnx_session is not None): ort_outs = self.onnx_session.run(None, X_train_batch) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_train_batch) else: X_test_batch = channel.X_test[prior_idx:idx] if (self.onnx_session is not None): ort_inputs = {self.onnx_session.get_inputs()[0].name: X_test_batch.astype(np.float32)} ort_outs = self.onnx_session.run(None, ort_inputs) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_test_batch) logger.debug('predict: batch ', i, ' - ', y_hat_batch.shape) self.aggregate_predictions(y_hat_batch) self.y_hat = np.reshape(self.y_hat, (self.y_hat.size,)) if Train: channel.y_train_hat = self.y_hat elif self.config.FFT: logger.info('FFT modelling') channel.y_hat = sp.fft.irfft(self.y_hat) else: channel.y_hat = self.y_hat if (Path is None): Path = '' np.save(os.path.join(Path, 'data', self.run_id, 'y_hat', '{}.npy'.format(self.chan_id)), self.y_hat) return channel
Used trained LSTM model to predict test data arriving in batches. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel Returns: channel (obj): Channel class object with y_hat values as attribute
telemanom/modeling.py
batch_predict
sedgewickmm18/telemanom
0
python
def batch_predict(self, channel, Train=False, Path=None): '\n Used trained LSTM model to predict test data arriving in batches.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Returns:\n channel (obj): Channel class object with y_hat values as attribute\n ' if Train: num_batches = int(((channel.y_train.shape[0] - self.config.l_s) / self.config.batch_size)) else: num_batches = int(((channel.y_test.shape[0] - self.config.l_s) / self.config.batch_size)) logger.debug('predict: num_batches ', num_batches) if (num_batches < 0): raise ValueError('l_s ({}) too large for stream length {}.'.format(self.config.l_s, channel.y_test.shape[0])) for i in range(0, (num_batches + 1)): prior_idx = (i * self.config.batch_size) idx = ((i + 1) * self.config.batch_size) if ((i + 1) == (num_batches + 1)): if Train: idx = channel.y_test.shape[0] else: idx = channel.y_train.shape[0] if Train: X_train_batch = channel.X_train[prior_idx:idx] if (self.onnx_session is not None): ort_outs = self.onnx_session.run(None, X_train_batch) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_train_batch) else: X_test_batch = channel.X_test[prior_idx:idx] if (self.onnx_session is not None): ort_inputs = {self.onnx_session.get_inputs()[0].name: X_test_batch.astype(np.float32)} ort_outs = self.onnx_session.run(None, ort_inputs) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_test_batch) logger.debug('predict: batch ', i, ' - ', y_hat_batch.shape) self.aggregate_predictions(y_hat_batch) self.y_hat = np.reshape(self.y_hat, (self.y_hat.size,)) if Train: channel.y_train_hat = self.y_hat elif self.config.FFT: logger.info('FFT modelling') channel.y_hat = sp.fft.irfft(self.y_hat) else: channel.y_hat = self.y_hat if (Path is None): Path = np.save(os.path.join(Path, 'data', self.run_id, 'y_hat', '{}.npy'.format(self.chan_id)), self.y_hat) return channel
def batch_predict(self, channel, Train=False, Path=None): '\n Used trained LSTM model to predict test data arriving in batches.\n\n Args:\n channel (obj): Channel class object containing train/test data\n for X,y for a single channel\n\n Returns:\n channel (obj): Channel class object with y_hat values as attribute\n ' if Train: num_batches = int(((channel.y_train.shape[0] - self.config.l_s) / self.config.batch_size)) else: num_batches = int(((channel.y_test.shape[0] - self.config.l_s) / self.config.batch_size)) logger.debug('predict: num_batches ', num_batches) if (num_batches < 0): raise ValueError('l_s ({}) too large for stream length {}.'.format(self.config.l_s, channel.y_test.shape[0])) for i in range(0, (num_batches + 1)): prior_idx = (i * self.config.batch_size) idx = ((i + 1) * self.config.batch_size) if ((i + 1) == (num_batches + 1)): if Train: idx = channel.y_test.shape[0] else: idx = channel.y_train.shape[0] if Train: X_train_batch = channel.X_train[prior_idx:idx] if (self.onnx_session is not None): ort_outs = self.onnx_session.run(None, X_train_batch) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_train_batch) else: X_test_batch = channel.X_test[prior_idx:idx] if (self.onnx_session is not None): ort_inputs = {self.onnx_session.get_inputs()[0].name: X_test_batch.astype(np.float32)} ort_outs = self.onnx_session.run(None, ort_inputs) y_hat_batch = ort_outs[0] else: (y_hat_batch, _) = self.model(X_test_batch) logger.debug('predict: batch ', i, ' - ', y_hat_batch.shape) self.aggregate_predictions(y_hat_batch) self.y_hat = np.reshape(self.y_hat, (self.y_hat.size,)) if Train: channel.y_train_hat = self.y_hat elif self.config.FFT: logger.info('FFT modelling') channel.y_hat = sp.fft.irfft(self.y_hat) else: channel.y_hat = self.y_hat if (Path is None): Path = np.save(os.path.join(Path, 'data', self.run_id, 'y_hat', '{}.npy'.format(self.chan_id)), self.y_hat) return channel<|docstring|>Used trained LSTM model to predict test data arriving in batches. Args: channel (obj): Channel class object containing train/test data for X,y for a single channel Returns: channel (obj): Channel class object with y_hat values as attribute<|endoftext|>
74d41f21b8ef8ddc31d7054478e6c5571b6c1c7f023d5740a83b3fb932ad8046
def __init__(__self__, *, encryption_algorithm: str, value: str, encryption_cert_thumbprint: Optional[str]=None): '\n Represent the secrets intended for encryption with asymmetric key pair.\n :param str encryption_algorithm: The algorithm used to encrypt "Value".\n :param str value: The value of the secret.\n :param str encryption_cert_thumbprint: Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.\n ' pulumi.set(__self__, 'encryption_algorithm', encryption_algorithm) pulumi.set(__self__, 'value', value) if (encryption_cert_thumbprint is not None): pulumi.set(__self__, 'encryption_cert_thumbprint', encryption_cert_thumbprint)
Represent the secrets intended for encryption with asymmetric key pair. :param str encryption_algorithm: The algorithm used to encrypt "Value". :param str value: The value of the secret. :param str encryption_cert_thumbprint: Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.
sdk/python/pulumi_azure_native/storsimple/outputs.py
__init__
pulumi-bot/pulumi-azure-native
31
python
def __init__(__self__, *, encryption_algorithm: str, value: str, encryption_cert_thumbprint: Optional[str]=None): '\n Represent the secrets intended for encryption with asymmetric key pair.\n :param str encryption_algorithm: The algorithm used to encrypt "Value".\n :param str value: The value of the secret.\n :param str encryption_cert_thumbprint: Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.\n ' pulumi.set(__self__, 'encryption_algorithm', encryption_algorithm) pulumi.set(__self__, 'value', value) if (encryption_cert_thumbprint is not None): pulumi.set(__self__, 'encryption_cert_thumbprint', encryption_cert_thumbprint)
def __init__(__self__, *, encryption_algorithm: str, value: str, encryption_cert_thumbprint: Optional[str]=None): '\n Represent the secrets intended for encryption with asymmetric key pair.\n :param str encryption_algorithm: The algorithm used to encrypt "Value".\n :param str value: The value of the secret.\n :param str encryption_cert_thumbprint: Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.\n ' pulumi.set(__self__, 'encryption_algorithm', encryption_algorithm) pulumi.set(__self__, 'value', value) if (encryption_cert_thumbprint is not None): pulumi.set(__self__, 'encryption_cert_thumbprint', encryption_cert_thumbprint)<|docstring|>Represent the secrets intended for encryption with asymmetric key pair. :param str encryption_algorithm: The algorithm used to encrypt "Value". :param str value: The value of the secret. :param str encryption_cert_thumbprint: Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.<|endoftext|>
33ef9ec755dda7c2696c55275316fd8b716cd45e27b8b88a10a77e4a73b2d7a6
@property @pulumi.getter(name='encryptionAlgorithm') def encryption_algorithm(self) -> str: '\n The algorithm used to encrypt "Value".\n ' return pulumi.get(self, 'encryption_algorithm')
The algorithm used to encrypt "Value".
sdk/python/pulumi_azure_native/storsimple/outputs.py
encryption_algorithm
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter(name='encryptionAlgorithm') def encryption_algorithm(self) -> str: '\n \n ' return pulumi.get(self, 'encryption_algorithm')
@property @pulumi.getter(name='encryptionAlgorithm') def encryption_algorithm(self) -> str: '\n \n ' return pulumi.get(self, 'encryption_algorithm')<|docstring|>The algorithm used to encrypt "Value".<|endoftext|>
a72b97818c5757b45e23443833e92535957f24e9405b4d1edad11cd16dede7a0
@property @pulumi.getter def value(self) -> str: '\n The value of the secret.\n ' return pulumi.get(self, 'value')
The value of the secret.
sdk/python/pulumi_azure_native/storsimple/outputs.py
value
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter def value(self) -> str: '\n \n ' return pulumi.get(self, 'value')
@property @pulumi.getter def value(self) -> str: '\n \n ' return pulumi.get(self, 'value')<|docstring|>The value of the secret.<|endoftext|>
5c563ba8474071a16f58934a68e897716d81b54e2dfe42b0a6ddf23ecc369258
@property @pulumi.getter(name='encryptionCertThumbprint') def encryption_cert_thumbprint(self) -> Optional[str]: '\n Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.\n ' return pulumi.get(self, 'encryption_cert_thumbprint')
Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.
sdk/python/pulumi_azure_native/storsimple/outputs.py
encryption_cert_thumbprint
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter(name='encryptionCertThumbprint') def encryption_cert_thumbprint(self) -> Optional[str]: '\n \n ' return pulumi.get(self, 'encryption_cert_thumbprint')
@property @pulumi.getter(name='encryptionCertThumbprint') def encryption_cert_thumbprint(self) -> Optional[str]: '\n \n ' return pulumi.get(self, 'encryption_cert_thumbprint')<|docstring|>Thumbprint certificate that was used to encrypt "Value". If the value in unencrypted, it will be null.<|endoftext|>
a41e87ace6e7900f90a28d6418c3d92eac2bf949788db8802dafe629cc18d615
def __init__(__self__, *, days: Sequence[str], rate_in_mbps: int, start: 'outputs.TimeResponse', stop: 'outputs.TimeResponse'): "\n The schedule for bandwidth setting.\n :param Sequence[str] days: The days of the week when this schedule is applicable.\n :param int rate_in_mbps: The rate in Mbps.\n :param 'TimeResponseArgs' start: The start time of the schedule.\n :param 'TimeResponseArgs' stop: The stop time of the schedule.\n " pulumi.set(__self__, 'days', days) pulumi.set(__self__, 'rate_in_mbps', rate_in_mbps) pulumi.set(__self__, 'start', start) pulumi.set(__self__, 'stop', stop)
The schedule for bandwidth setting. :param Sequence[str] days: The days of the week when this schedule is applicable. :param int rate_in_mbps: The rate in Mbps. :param 'TimeResponseArgs' start: The start time of the schedule. :param 'TimeResponseArgs' stop: The stop time of the schedule.
sdk/python/pulumi_azure_native/storsimple/outputs.py
__init__
pulumi-bot/pulumi-azure-native
31
python
def __init__(__self__, *, days: Sequence[str], rate_in_mbps: int, start: 'outputs.TimeResponse', stop: 'outputs.TimeResponse'): "\n The schedule for bandwidth setting.\n :param Sequence[str] days: The days of the week when this schedule is applicable.\n :param int rate_in_mbps: The rate in Mbps.\n :param 'TimeResponseArgs' start: The start time of the schedule.\n :param 'TimeResponseArgs' stop: The stop time of the schedule.\n " pulumi.set(__self__, 'days', days) pulumi.set(__self__, 'rate_in_mbps', rate_in_mbps) pulumi.set(__self__, 'start', start) pulumi.set(__self__, 'stop', stop)
def __init__(__self__, *, days: Sequence[str], rate_in_mbps: int, start: 'outputs.TimeResponse', stop: 'outputs.TimeResponse'): "\n The schedule for bandwidth setting.\n :param Sequence[str] days: The days of the week when this schedule is applicable.\n :param int rate_in_mbps: The rate in Mbps.\n :param 'TimeResponseArgs' start: The start time of the schedule.\n :param 'TimeResponseArgs' stop: The stop time of the schedule.\n " pulumi.set(__self__, 'days', days) pulumi.set(__self__, 'rate_in_mbps', rate_in_mbps) pulumi.set(__self__, 'start', start) pulumi.set(__self__, 'stop', stop)<|docstring|>The schedule for bandwidth setting. :param Sequence[str] days: The days of the week when this schedule is applicable. :param int rate_in_mbps: The rate in Mbps. :param 'TimeResponseArgs' start: The start time of the schedule. :param 'TimeResponseArgs' stop: The stop time of the schedule.<|endoftext|>
c2418695034b5aa45c313188df5302ee0baa29e5ade01e84100eaba31c4fd89c
@property @pulumi.getter def days(self) -> Sequence[str]: '\n The days of the week when this schedule is applicable.\n ' return pulumi.get(self, 'days')
The days of the week when this schedule is applicable.
sdk/python/pulumi_azure_native/storsimple/outputs.py
days
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter def days(self) -> Sequence[str]: '\n \n ' return pulumi.get(self, 'days')
@property @pulumi.getter def days(self) -> Sequence[str]: '\n \n ' return pulumi.get(self, 'days')<|docstring|>The days of the week when this schedule is applicable.<|endoftext|>
76f52346508bf7dceed7d4aea14ba5ab4c354270e8d3605a065e08a3de17ff70
@property @pulumi.getter(name='rateInMbps') def rate_in_mbps(self) -> int: '\n The rate in Mbps.\n ' return pulumi.get(self, 'rate_in_mbps')
The rate in Mbps.
sdk/python/pulumi_azure_native/storsimple/outputs.py
rate_in_mbps
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter(name='rateInMbps') def rate_in_mbps(self) -> int: '\n \n ' return pulumi.get(self, 'rate_in_mbps')
@property @pulumi.getter(name='rateInMbps') def rate_in_mbps(self) -> int: '\n \n ' return pulumi.get(self, 'rate_in_mbps')<|docstring|>The rate in Mbps.<|endoftext|>
e93b02335adb81368019c807a19a5a53f2a31e5c34edbff1216e0916a3087962
@property @pulumi.getter def start(self) -> 'outputs.TimeResponse': '\n The start time of the schedule.\n ' return pulumi.get(self, 'start')
The start time of the schedule.
sdk/python/pulumi_azure_native/storsimple/outputs.py
start
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter def start(self) -> 'outputs.TimeResponse': '\n \n ' return pulumi.get(self, 'start')
@property @pulumi.getter def start(self) -> 'outputs.TimeResponse': '\n \n ' return pulumi.get(self, 'start')<|docstring|>The start time of the schedule.<|endoftext|>
210083d0ee5e8470dd23e897b01c185460dab15042d0617072eeaae85bf3dca6
@property @pulumi.getter def stop(self) -> 'outputs.TimeResponse': '\n The stop time of the schedule.\n ' return pulumi.get(self, 'stop')
The stop time of the schedule.
sdk/python/pulumi_azure_native/storsimple/outputs.py
stop
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter def stop(self) -> 'outputs.TimeResponse': '\n \n ' return pulumi.get(self, 'stop')
@property @pulumi.getter def stop(self) -> 'outputs.TimeResponse': '\n \n ' return pulumi.get(self, 'stop')<|docstring|>The stop time of the schedule.<|endoftext|>
fca50fda1a74c790d7b302867e99845f94cfec640a797167476dc0e79f7bc59e
def __init__(__self__, *, error_message: Optional[str]=None, is_eligible_for_failover: Optional[bool]=None): '\n The eligibility result of failover set, for failover.\n :param str error_message: The error message, if the failover set is not eligible for failover.\n :param bool is_eligible_for_failover: Represents if this failover set is eligible for failover or not.\n ' if (error_message is not None): pulumi.set(__self__, 'error_message', error_message) if (is_eligible_for_failover is not None): pulumi.set(__self__, 'is_eligible_for_failover', is_eligible_for_failover)
The eligibility result of failover set, for failover. :param str error_message: The error message, if the failover set is not eligible for failover. :param bool is_eligible_for_failover: Represents if this failover set is eligible for failover or not.
sdk/python/pulumi_azure_native/storsimple/outputs.py
__init__
pulumi-bot/pulumi-azure-native
31
python
def __init__(__self__, *, error_message: Optional[str]=None, is_eligible_for_failover: Optional[bool]=None): '\n The eligibility result of failover set, for failover.\n :param str error_message: The error message, if the failover set is not eligible for failover.\n :param bool is_eligible_for_failover: Represents if this failover set is eligible for failover or not.\n ' if (error_message is not None): pulumi.set(__self__, 'error_message', error_message) if (is_eligible_for_failover is not None): pulumi.set(__self__, 'is_eligible_for_failover', is_eligible_for_failover)
def __init__(__self__, *, error_message: Optional[str]=None, is_eligible_for_failover: Optional[bool]=None): '\n The eligibility result of failover set, for failover.\n :param str error_message: The error message, if the failover set is not eligible for failover.\n :param bool is_eligible_for_failover: Represents if this failover set is eligible for failover or not.\n ' if (error_message is not None): pulumi.set(__self__, 'error_message', error_message) if (is_eligible_for_failover is not None): pulumi.set(__self__, 'is_eligible_for_failover', is_eligible_for_failover)<|docstring|>The eligibility result of failover set, for failover. :param str error_message: The error message, if the failover set is not eligible for failover. :param bool is_eligible_for_failover: Represents if this failover set is eligible for failover or not.<|endoftext|>
b914ce06e2ac41c5b4ac85bb59b78173884fcb6fceb6fdfe680d3ce8d3d7df3e
@property @pulumi.getter(name='errorMessage') def error_message(self) -> Optional[str]: '\n The error message, if the failover set is not eligible for failover.\n ' return pulumi.get(self, 'error_message')
The error message, if the failover set is not eligible for failover.
sdk/python/pulumi_azure_native/storsimple/outputs.py
error_message
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter(name='errorMessage') def error_message(self) -> Optional[str]: '\n \n ' return pulumi.get(self, 'error_message')
@property @pulumi.getter(name='errorMessage') def error_message(self) -> Optional[str]: '\n \n ' return pulumi.get(self, 'error_message')<|docstring|>The error message, if the failover set is not eligible for failover.<|endoftext|>
09457cc4d41e8950d2e6b2ecf27e19b2fe1bbfde091ea2f76d4fb565338c53ac
@property @pulumi.getter(name='isEligibleForFailover') def is_eligible_for_failover(self) -> Optional[bool]: '\n Represents if this failover set is eligible for failover or not.\n ' return pulumi.get(self, 'is_eligible_for_failover')
Represents if this failover set is eligible for failover or not.
sdk/python/pulumi_azure_native/storsimple/outputs.py
is_eligible_for_failover
pulumi-bot/pulumi-azure-native
31
python
@property @pulumi.getter(name='isEligibleForFailover') def is_eligible_for_failover(self) -> Optional[bool]: '\n \n ' return pulumi.get(self, 'is_eligible_for_failover')
@property @pulumi.getter(name='isEligibleForFailover') def is_eligible_for_failover(self) -> Optional[bool]: '\n \n ' return pulumi.get(self, 'is_eligible_for_failover')<|docstring|>Represents if this failover set is eligible for failover or not.<|endoftext|>
964a76c98cf3ea728f0a341c0fdfc781cd8842f2f245f4e6d264d2ee8fc2f4b6
def __init__(__self__, *, eligibility_result: Optional['outputs.FailoverSetEligibilityResultResponseResult']=None, volume_containers: Optional[Sequence['outputs.VolumeContainerFailoverMetadataResponseResult']]=None): "\n The failover set on a device.\n :param 'FailoverSetEligibilityResultResponseArgs' eligibility_result: The eligibility result of the failover set, for failover.\n :param Sequence['VolumeContainerFailoverMetadataResponseArgs'] volume_containers: The list of meta data of volume containers, which are part of the failover set.\n " if (eligibility_result is not None): pulumi.set(__self__, 'eligibility_result', eligibility_result) if (volume_containers is not None): pulumi.set(__self__, 'volume_containers', volume_containers)
The failover set on a device. :param 'FailoverSetEligibilityResultResponseArgs' eligibility_result: The eligibility result of the failover set, for failover. :param Sequence['VolumeContainerFailoverMetadataResponseArgs'] volume_containers: The list of meta data of volume containers, which are part of the failover set.
sdk/python/pulumi_azure_native/storsimple/outputs.py
__init__
pulumi-bot/pulumi-azure-native
31
python
def __init__(__self__, *, eligibility_result: Optional['outputs.FailoverSetEligibilityResultResponseResult']=None, volume_containers: Optional[Sequence['outputs.VolumeContainerFailoverMetadataResponseResult']]=None): "\n The failover set on a device.\n :param 'FailoverSetEligibilityResultResponseArgs' eligibility_result: The eligibility result of the failover set, for failover.\n :param Sequence['VolumeContainerFailoverMetadataResponseArgs'] volume_containers: The list of meta data of volume containers, which are part of the failover set.\n " if (eligibility_result is not None): pulumi.set(__self__, 'eligibility_result', eligibility_result) if (volume_containers is not None): pulumi.set(__self__, 'volume_containers', volume_containers)
def __init__(__self__, *, eligibility_result: Optional['outputs.FailoverSetEligibilityResultResponseResult']=None, volume_containers: Optional[Sequence['outputs.VolumeContainerFailoverMetadataResponseResult']]=None): "\n The failover set on a device.\n :param 'FailoverSetEligibilityResultResponseArgs' eligibility_result: The eligibility result of the failover set, for failover.\n :param Sequence['VolumeContainerFailoverMetadataResponseArgs'] volume_containers: The list of meta data of volume containers, which are part of the failover set.\n " if (eligibility_result is not None): pulumi.set(__self__, 'eligibility_result', eligibility_result) if (volume_containers is not None): pulumi.set(__self__, 'volume_containers', volume_containers)<|docstring|>The failover set on a device. :param 'FailoverSetEligibilityResultResponseArgs' eligibility_result: The eligibility result of the failover set, for failover. :param Sequence['VolumeContainerFailoverMetadataResponseArgs'] volume_containers: The list of meta data of volume containers, which are part of the failover set.<|endoftext|>