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{
"filename": "test_caching.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/tests/unit_tests/embeddings/test_caching.py",
"type": "Python"
}
|
"""Embeddings tests."""
from typing import List
import pytest
from langchain_core.embeddings import Embeddings
from langchain.embeddings import CacheBackedEmbeddings
from langchain.storage.in_memory import InMemoryStore
class MockEmbeddings(Embeddings):
def embed_documents(self, texts: List[str]) -> List[List[float]]:
# Simulate embedding documents
embeddings: List[List[float]] = []
for text in texts:
if text == "RAISE_EXCEPTION":
raise ValueError("Simulated embedding failure")
embeddings.append([len(text), len(text) + 1])
return embeddings
def embed_query(self, text: str) -> List[float]:
# Simulate embedding a query
return [5.0, 6.0]
@pytest.fixture
def cache_embeddings() -> CacheBackedEmbeddings:
"""Create a cache backed embeddings."""
store = InMemoryStore()
embeddings = MockEmbeddings()
return CacheBackedEmbeddings.from_bytes_store(
embeddings, store, namespace="test_namespace"
)
@pytest.fixture
def cache_embeddings_batch() -> CacheBackedEmbeddings:
"""Create a cache backed embeddings with a batch_size of 3."""
store = InMemoryStore()
embeddings = MockEmbeddings()
return CacheBackedEmbeddings.from_bytes_store(
embeddings, store, namespace="test_namespace", batch_size=3
)
@pytest.fixture
def cache_embeddings_with_query() -> CacheBackedEmbeddings:
"""Create a cache backed embeddings with query caching."""
doc_store = InMemoryStore()
query_store = InMemoryStore()
embeddings = MockEmbeddings()
return CacheBackedEmbeddings.from_bytes_store(
embeddings,
document_embedding_cache=doc_store,
namespace="test_namespace",
query_embedding_cache=query_store,
)
def test_embed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
texts = ["1", "22", "a", "333"]
vectors = cache_embeddings.embed_documents(texts)
expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]]
assert vectors == expected_vectors
keys = list(cache_embeddings.document_embedding_store.yield_keys())
assert len(keys) == 4
# UUID is expected to be the same for the same text
assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
def test_embed_documents_batch(cache_embeddings_batch: CacheBackedEmbeddings) -> None:
# "RAISE_EXCEPTION" forces a failure in batch 2
texts = ["1", "22", "a", "333", "RAISE_EXCEPTION"]
try:
cache_embeddings_batch.embed_documents(texts)
except ValueError:
pass
keys = list(cache_embeddings_batch.document_embedding_store.yield_keys())
# only the first batch of three embeddings should exist
assert len(keys) == 3
# UUID is expected to be the same for the same text
assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
text = "query_text"
vector = cache_embeddings.embed_query(text)
expected_vector = [5.0, 6.0]
assert vector == expected_vector
assert cache_embeddings.query_embedding_store is None
def test_embed_cached_query(cache_embeddings_with_query: CacheBackedEmbeddings) -> None:
text = "query_text"
vector = cache_embeddings_with_query.embed_query(text)
expected_vector = [5.0, 6.0]
assert vector == expected_vector
keys = list(cache_embeddings_with_query.query_embedding_store.yield_keys()) # type: ignore[union-attr]
assert len(keys) == 1
assert keys[0] == "test_namespace89ec3dae-a4d9-5636-a62e-ff3b56cdfa15"
async def test_aembed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
texts = ["1", "22", "a", "333"]
vectors = await cache_embeddings.aembed_documents(texts)
expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]]
assert vectors == expected_vectors
keys = [
key async for key in cache_embeddings.document_embedding_store.ayield_keys()
]
assert len(keys) == 4
# UUID is expected to be the same for the same text
assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
async def test_aembed_documents_batch(
cache_embeddings_batch: CacheBackedEmbeddings,
) -> None:
# "RAISE_EXCEPTION" forces a failure in batch 2
texts = ["1", "22", "a", "333", "RAISE_EXCEPTION"]
try:
await cache_embeddings_batch.aembed_documents(texts)
except ValueError:
pass
keys = [
key
async for key in cache_embeddings_batch.document_embedding_store.ayield_keys()
]
# only the first batch of three embeddings should exist
assert len(keys) == 3
# UUID is expected to be the same for the same text
assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
async def test_aembed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
text = "query_text"
vector = await cache_embeddings.aembed_query(text)
expected_vector = [5.0, 6.0]
assert vector == expected_vector
async def test_aembed_query_cached(
cache_embeddings_with_query: CacheBackedEmbeddings,
) -> None:
text = "query_text"
await cache_embeddings_with_query.aembed_query(text)
keys = list(cache_embeddings_with_query.query_embedding_store.yield_keys()) # type: ignore[union-attr]
assert len(keys) == 1
assert keys[0] == "test_namespace89ec3dae-a4d9-5636-a62e-ff3b56cdfa15"
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@tests@unit_tests@embeddings@test_caching.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "esheldon/ngmix",
"repo_path": "ngmix_extracted/ngmix-master/ngmix/admom/__init__.py",
"type": "Python"
}
|
# flake8: noqa
from . import admom
from .admom import *
from . import admom_nb
|
esheldonREPO_NAMEngmixPATH_START.@ngmix_extracted@ngmix-master@ngmix@admom@__init__.py@.PATH_END.py
|
{
"filename": "_bordercolor.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/layout/slider/_bordercolor.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class BordercolorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self, plotly_name="bordercolor", parent_name="layout.slider", **kwargs
):
super(BordercolorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "arraydraw"),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@layout@slider@_bordercolor.py@.PATH_END.py
|
{
"filename": "_lightposition.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/isosurface/_lightposition.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Lightposition(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "isosurface"
_path_str = "isosurface.lightposition"
_valid_props = {"x", "y", "z"}
# x
# -
@property
def x(self):
"""
Numeric vector, representing the X coordinate for each vertex.
The 'x' property is a number and may be specified as:
- An int or float in the interval [-100000, 100000]
Returns
-------
int|float
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# y
# -
@property
def y(self):
"""
Numeric vector, representing the Y coordinate for each vertex.
The 'y' property is a number and may be specified as:
- An int or float in the interval [-100000, 100000]
Returns
-------
int|float
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# z
# -
@property
def z(self):
"""
Numeric vector, representing the Z coordinate for each vertex.
The 'z' property is a number and may be specified as:
- An int or float in the interval [-100000, 100000]
Returns
-------
int|float
"""
return self["z"]
@z.setter
def z(self, val):
self["z"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
x
Numeric vector, representing the X coordinate for each
vertex.
y
Numeric vector, representing the Y coordinate for each
vertex.
z
Numeric vector, representing the Z coordinate for each
vertex.
"""
def __init__(self, arg=None, x=None, y=None, z=None, **kwargs):
"""
Construct a new Lightposition object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.isosurface.Lightposition`
x
Numeric vector, representing the X coordinate for each
vertex.
y
Numeric vector, representing the Y coordinate for each
vertex.
z
Numeric vector, representing the Z coordinate for each
vertex.
Returns
-------
Lightposition
"""
super(Lightposition, self).__init__("lightposition")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.isosurface.Lightposition
constructor must be a dict or
an instance of :class:`plotly.graph_objs.isosurface.Lightposition`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("x", None)
_v = x if x is not None else _v
if _v is not None:
self["x"] = _v
_v = arg.pop("y", None)
_v = y if y is not None else _v
if _v is not None:
self["y"] = _v
_v = arg.pop("z", None)
_v = z if z is not None else _v
if _v is not None:
self["z"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@isosurface@_lightposition.py@.PATH_END.py
|
{
"filename": "io_ops.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/ops/io_ops.py",
"type": "Python"
}
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=line-too-long
"""Inputs and Readers.
See the [Inputs and
Readers](https://tensorflow.org/api_guides/python/io_ops) guide.
"""
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor as tensor_lib
from tensorflow.python.lib.io import python_io
from tensorflow.python.ops import gen_data_flow_ops
from tensorflow.python.ops import gen_io_ops
from tensorflow.python.ops import gen_parsing_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_io_ops import *
# pylint: enable=wildcard-import
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
def _save(filename, tensor_names, tensors, tensor_slices=None, name="save"):
"""Save a list of tensors to a file with given names.
Example usage without slice info:
Save("/foo/bar", ["w", "b"], [w, b])
Example usage with slices:
Save("/foo/bar", ["w", "w"], [slice0, slice1],
tensor_slices=["4 10 0,2:-", "4 10 2,2:-"])
Args:
filename: the file name of the sstable.
tensor_names: a list of strings.
tensors: the list of tensors to be saved.
tensor_slices: Optional list of strings to specify the shape and slices of
a larger virtual tensor that each tensor is a part of. If not specified
each tensor is saved as a full slice.
name: string. Optional name for the op.
Requires:
The length of tensors should match the size of tensor_names and of
tensor_slices.
Returns:
An Operation that saves the tensors.
"""
if tensor_slices is None:
return gen_io_ops.save(filename, tensor_names, tensors, name=name)
else:
return gen_io_ops.save_slices(filename, tensor_names, tensor_slices,
tensors, name=name)
def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type,
name="restore_slice", preferred_shard=-1):
"""Restore a tensor slice from a set of files with a given pattern.
Example usage:
RestoreSlice("/foo/bar-?????-of-?????", "w", "10 10 0,2:-", DT_FLOAT)
Args:
file_pattern: the file pattern used to match a set of checkpoint files.
tensor_name: the name of the tensor to restore.
shape_and_slice: the shape-and-slice spec of the slice.
tensor_type: the type of the tensor to restore.
name: string. Optional name for the op.
preferred_shard: Int. Optional shard to open first in the checkpoint file.
Returns:
A tensor of type "tensor_type".
"""
base_type = dtypes.as_dtype(tensor_type).base_dtype
return gen_io_ops.restore_slice(
file_pattern, tensor_name, shape_and_slice, base_type,
preferred_shard, name=name)
@tf_export("io.read_file", v1=["io.read_file", "read_file"])
def read_file(filename, name=None):
"""Reads the contents of file.
This operation returns a tensor with the entire contents of the input
filename. It does not do any parsing, it just returns the contents as
they are. Usually, this is the first step in the input pipeline.
Example:
>>> with open("/tmp/file.txt", "w") as f:
... f.write("asdf")
...
4
>>> tf.io.read_file("/tmp/file.txt")
<tf.Tensor: shape=(), dtype=string, numpy=b'asdf'>
Example of using the op in a function to read an image, decode it and reshape
the tensor containing the pixel data:
>>> @tf.function
... def load_image(filename):
... raw = tf.io.read_file(filename)
... image = tf.image.decode_png(raw, channels=3)
... # the `print` executes during tracing.
... print("Initial shape: ", image.shape)
... image.set_shape([28, 28, 3])
... print("Final shape: ", image.shape)
... return image
Args:
filename: string. filename to read from.
name: string. Optional name for the op.
Returns:
A tensor of dtype "string", with the file contents.
"""
return gen_io_ops.read_file(filename, name)
@tf_export(
"io.serialize_tensor", v1=["io.serialize_tensor", "serialize_tensor"])
def serialize_tensor(tensor, name=None):
r"""Transforms a Tensor into a serialized TensorProto proto.
This operation transforms data in a `tf.Tensor` into a `tf.Tensor` of type
`tf.string` containing the data in a binary string in little-endian format.
This operation can transform scalar data and linear arrays, but it is most
useful in converting multidimensional arrays into a format accepted by binary
storage formats such as a `TFRecord` or `tf.train.Example`.
See also:
- `tf.io.parse_tensor`: inverse operation of `tf.io.serialize_tensor` that
transforms a scalar string containing a serialized Tensor in little-endian
format into a Tensor of a specified type.
- `tf.ensure_shape`: `parse_tensor` cannot statically determine the shape of
the parsed tensor. Use `tf.ensure_shape` to set the static shape when running
under a `tf.function`
- `.SerializeToString`, serializes a proto to a binary-string
Example of serializing scalar data:
>>> t = tf.constant(1)
>>> tf.io.serialize_tensor(t)
<tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
Example of storing non-scalar data into a `tf.train.Example`:
>>> t1 = [[1, 2]]
>>> t2 = [[7, 8]]
>>> nonscalar = tf.concat([t1, t2], 0)
>>> nonscalar
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
[7, 8]], dtype=int32)>
Serialize the data using `tf.io.serialize_tensor`.
>>> serialized_nonscalar = tf.io.serialize_tensor(nonscalar)
>>> serialized_nonscalar
<tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
Store the data in a `tf.train.Feature`.
>>> feature_of_bytes = tf.train.Feature(
... bytes_list=tf.train.BytesList(value=[serialized_nonscalar.numpy()]))
>>> feature_of_bytes
bytes_list {
value: "\010...\000"
}
Put the `tf.train.Feature` message into a `tf.train.Example`.
>>> features_for_example = {
... 'feature0': feature_of_bytes
... }
>>> example_proto = tf.train.Example(
... features=tf.train.Features(feature=features_for_example))
>>> example_proto
features {
feature {
key: "feature0"
value {
bytes_list {
value: "\010...\000"
}
}
}
}
Args:
tensor: A `tf.Tensor`.
name: string. Optional name for the op.
Returns:
A Tensor of dtype string.
"""
return gen_parsing_ops.serialize_tensor(tensor, name)
@tf_export(v1=["ReaderBase"])
class ReaderBase:
"""Base class for different Reader types, that produce a record every step.
Conceptually, Readers convert string 'work units' into records (key,
value pairs). Typically the 'work units' are filenames and the
records are extracted from the contents of those files. We want a
single record produced per step, but a work unit can correspond to
many records.
Therefore we introduce some decoupling using a queue. The queue
contains the work units and the Reader dequeues from the queue when
it is asked to produce a record (via Read()) but it has finished the
last work unit.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
def __init__(self, reader_ref, supports_serialize=False):
"""Creates a new ReaderBase.
Args:
reader_ref: The operation that implements the reader.
supports_serialize: True if the reader implementation can
serialize its state.
Raises:
RuntimeError: If eager execution is enabled.
"""
if context.executing_eagerly():
raise RuntimeError(
"Readers are not supported when eager execution is enabled. "
"Instead, please use tf.data to get data into your model.")
self._reader_ref = reader_ref
self._supports_serialize = supports_serialize
@property
def reader_ref(self):
"""Op that implements the reader."""
return self._reader_ref
def read(self, queue, name=None):
"""Returns the next record (key, value) pair produced by a reader.
Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).
Args:
queue: A Queue or a mutable string Tensor representing a handle
to a Queue, with string work items.
name: A name for the operation (optional).
Returns:
A tuple of Tensors (key, value).
key: A string scalar Tensor.
value: A string scalar Tensor.
"""
if isinstance(queue, tensor_lib.Tensor):
queue_ref = queue
else:
queue_ref = queue.queue_ref
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_read_v2(self._reader_ref, queue_ref, name=name)
else:
# For compatibility with pre-resource queues, create a ref(string) tensor
# which can be looked up as the same queue by a resource manager.
old_queue_op = gen_data_flow_ops.fake_queue(queue_ref)
return gen_io_ops.reader_read(self._reader_ref, old_queue_op, name=name)
def read_up_to(self, queue, num_records, # pylint: disable=invalid-name
name=None):
"""Returns up to num_records (key, value) pairs produced by a reader.
Will dequeue a work unit from queue if necessary (e.g., when the
Reader needs to start reading from a new file since it has
finished with the previous file).
It may return less than num_records even before the last batch.
Args:
queue: A Queue or a mutable string Tensor representing a handle
to a Queue, with string work items.
num_records: Number of records to read.
name: A name for the operation (optional).
Returns:
A tuple of Tensors (keys, values).
keys: A 1-D string Tensor.
values: A 1-D string Tensor.
"""
if isinstance(queue, tensor_lib.Tensor):
queue_ref = queue
else:
queue_ref = queue.queue_ref
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_read_up_to_v2(self._reader_ref,
queue_ref,
num_records,
name=name)
else:
# For compatibility with pre-resource queues, create a ref(string) tensor
# which can be looked up as the same queue by a resource manager.
old_queue_op = gen_data_flow_ops.fake_queue(queue_ref)
return gen_io_ops.reader_read_up_to(self._reader_ref,
old_queue_op,
num_records,
name=name)
def num_records_produced(self, name=None):
"""Returns the number of records this reader has produced.
This is the same as the number of Read executions that have
succeeded.
Args:
name: A name for the operation (optional).
Returns:
An int64 Tensor.
"""
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_num_records_produced_v2(self._reader_ref,
name=name)
else:
return gen_io_ops.reader_num_records_produced(self._reader_ref,
name=name)
def num_work_units_completed(self, name=None):
"""Returns the number of work units this reader has finished processing.
Args:
name: A name for the operation (optional).
Returns:
An int64 Tensor.
"""
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_num_work_units_completed_v2(self._reader_ref,
name=name)
else:
return gen_io_ops.reader_num_work_units_completed(self._reader_ref,
name=name)
def serialize_state(self, name=None):
"""Produce a string tensor that encodes the state of a reader.
Not all Readers support being serialized, so this can produce an
Unimplemented error.
Args:
name: A name for the operation (optional).
Returns:
A string Tensor.
"""
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_serialize_state_v2(self._reader_ref, name=name)
else:
return gen_io_ops.reader_serialize_state(self._reader_ref, name=name)
def restore_state(self, state, name=None):
"""Restore a reader to a previously saved state.
Not all Readers support being restored, so this can produce an
Unimplemented error.
Args:
state: A string Tensor.
Result of a SerializeState of a Reader with matching type.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_restore_state_v2(
self._reader_ref, state, name=name)
else:
return gen_io_ops.reader_restore_state(self._reader_ref, state, name=name)
@property
def supports_serialize(self):
"""Whether the Reader implementation can serialize its state."""
return self._supports_serialize
def reset(self, name=None):
"""Restore a reader to its initial clean state.
Args:
name: A name for the operation (optional).
Returns:
The created Operation.
"""
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_reset_v2(self._reader_ref, name=name)
else:
return gen_io_ops.reader_reset(self._reader_ref, name=name)
ops.NotDifferentiable("ReaderRead")
ops.NotDifferentiable("ReaderReadUpTo")
ops.NotDifferentiable("ReaderNumRecordsProduced")
ops.NotDifferentiable("ReaderNumWorkUnitsCompleted")
ops.NotDifferentiable("ReaderSerializeState")
ops.NotDifferentiable("ReaderRestoreState")
ops.NotDifferentiable("ReaderReset")
@tf_export(v1=["WholeFileReader"])
class WholeFileReader(ReaderBase):
"""A Reader that outputs the entire contents of a file as a value.
To use, enqueue filenames in a Queue. The output of Read will
be a filename (key) and the contents of that file (value).
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.data.Dataset.map(tf.read_file)`.")
def __init__(self, name=None):
"""Create a WholeFileReader.
Args:
name: A name for the operation (optional).
"""
rr = gen_io_ops.whole_file_reader_v2(name=name)
super(WholeFileReader, self).__init__(rr, supports_serialize=True)
ops.NotDifferentiable("WholeFileReader")
@tf_export(v1=["TextLineReader"])
class TextLineReader(ReaderBase):
"""A Reader that outputs the lines of a file delimited by newlines.
Newlines are stripped from the output.
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
# TODO(josh11b): Support serializing and restoring state.
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.data.TextLineDataset`.")
def __init__(self, skip_header_lines=None, name=None):
"""Create a TextLineReader.
Args:
skip_header_lines: An optional int. Defaults to 0. Number of lines
to skip from the beginning of every file.
name: A name for the operation (optional).
"""
rr = gen_io_ops.text_line_reader_v2(skip_header_lines=skip_header_lines,
name=name)
super(TextLineReader, self).__init__(rr)
ops.NotDifferentiable("TextLineReader")
@tf_export(v1=["FixedLengthRecordReader"])
class FixedLengthRecordReader(ReaderBase):
"""A Reader that outputs fixed-length records from a file.
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
# TODO(josh11b): Support serializing and restoring state.
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.data.FixedLengthRecordDataset`.")
def __init__(self,
record_bytes,
header_bytes=None,
footer_bytes=None,
hop_bytes=None,
name=None,
encoding=None):
"""Create a FixedLengthRecordReader.
Args:
record_bytes: An int.
header_bytes: An optional int. Defaults to 0.
footer_bytes: An optional int. Defaults to 0.
hop_bytes: An optional int. Defaults to 0.
name: A name for the operation (optional).
encoding: The type of encoding for the file. Defaults to none.
"""
rr = gen_io_ops.fixed_length_record_reader_v2(
record_bytes=record_bytes,
header_bytes=header_bytes,
footer_bytes=footer_bytes,
hop_bytes=hop_bytes,
encoding=encoding,
name=name)
super(FixedLengthRecordReader, self).__init__(rr)
ops.NotDifferentiable("FixedLengthRecordReader")
@tf_export(v1=["TFRecordReader"])
class TFRecordReader(ReaderBase):
"""A Reader that outputs the records from a TFRecords file.
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
# TODO(josh11b): Support serializing and restoring state.
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.data.TFRecordDataset`.")
def __init__(self, name=None, options=None):
"""Create a TFRecordReader.
Args:
name: A name for the operation (optional).
options: A TFRecordOptions object (optional).
"""
compression_type = python_io.TFRecordOptions.get_compression_type_string(
options)
rr = gen_io_ops.tf_record_reader_v2(
name=name, compression_type=compression_type)
super(TFRecordReader, self).__init__(rr)
ops.NotDifferentiable("TFRecordReader")
@tf_export(v1=["LMDBReader"])
class LMDBReader(ReaderBase):
"""A Reader that outputs the records from a LMDB file.
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.contrib.data.LMDBDataset`.")
def __init__(self, name=None, options=None):
"""Create a LMDBReader.
Args:
name: A name for the operation (optional).
options: A LMDBRecordOptions object (optional).
"""
del options
rr = gen_io_ops.lmdb_reader(name=name)
super(LMDBReader, self).__init__(rr)
ops.NotDifferentiable("LMDBReader")
@tf_export(v1=["IdentityReader"])
class IdentityReader(ReaderBase):
"""A Reader that outputs the queued work as both the key and value.
To use, enqueue strings in a Queue. Read will take the front
work string and output (work, work).
See ReaderBase for supported methods.
@compatibility(eager)
Readers are not compatible with eager execution. Instead, please
use `tf.data` to get data into your model.
@end_compatibility
"""
@deprecation.deprecated(
None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
"`tf.data.Dataset.map(...)`.")
def __init__(self, name=None):
"""Create a IdentityReader.
Args:
name: A name for the operation (optional).
"""
rr = gen_io_ops.identity_reader_v2(name=name)
super(IdentityReader, self).__init__(rr, supports_serialize=True)
ops.NotDifferentiable("IdentityReader")
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@ops@io_ops.py@.PATH_END.py
|
{
"filename": "compute_correction_function.py",
"repo_name": "oliverphilcox/HIPSTER",
"repo_path": "HIPSTER_extracted/HIPSTER-master/python/compute_correction_function.py",
"type": "Python"
}
|
## Function to compute the survey correction function for a given survey geometry (specified an input random particle file).
## This is based on a simple wrapper for Corrfunc, to compute RR counts which are compared to an idealized model
## NB: This requires an aperiodic survey.
## Results are stored as fits to the multipoles of 1/Phi = RR_true / RR_model, which is read by the C++ code
import sys
import numpy as np
# PARAMETERS
if len(sys.argv)!=7:
print("Usage: python compute_correction_function.py {RANDOM_PARTICLE_FILE} {OUTFILE} {R_MAX} {N_R_BINS} {N_MU_BINS} {NTHREADS}")
sys.exit()
fname = str(sys.argv[1])
outfile = str(sys.argv[2])
r_max = float(sys.argv[3])
nrbins = int(sys.argv[4])
nmu_bins = int(sys.argv[5])
nthreads = int(sys.argv[6])
mu_max = 1.;
## First read in weights and positions:
dtype = np.double
print("Counting lines in file")
total_lines=0
for n, line in enumerate(open(fname, 'r')):
total_lines+=1
X,Y,Z,W=[np.zeros(total_lines) for _ in range(4)]
print("Reading in data");
for n, line in enumerate(open(fname, 'r')):
if n%1000000==0:
print("Reading line %d of %d" %(n,total_lines))
split_line=np.array(line.split(" "), dtype=float)
X[n]=split_line[0];
Y[n]=split_line[1];
Z[n]=split_line[2];
W[n]=split_line[3];
N = len(X) # number of particles
print("Number of random particles %.1e"%N)
print('Computing pair counts up to a maximum radius of %.2f'%r_max)
binfile = np.linspace(0,r_max,nrbins+1)
binfile[0]=1e-4 # to avoid zero errors
r_hi = binfile[1:]
r_lo = binfile[:-1]
r_cen = 0.5*(r_lo+r_hi)
# Compute RR counts for the non-periodic case (measuring mu from the radial direction)
def coord_transform(x,y,z):
# Convert the X,Y,Z coordinates into Ra,Dec,comoving_distance (for use in corrfunc)
# Shamelessly stolen from astropy
xsq = x ** 2.
ysq = y ** 2.
zsq = z ** 2.
com_dist = (xsq + ysq + zsq) ** 0.5
s = (xsq + ysq) ** 0.5
if np.isscalar(x) and np.isscalar(y) and np.isscalar(z):
Ra = math.atan2(y, x)*180./np.pi
Dec = math.atan2(z, s)*180./np.pi
else:
Ra = np.arctan2(y, x)*180./np.pi+180.
Dec = np.arctan2(z, s)*180./np.pi
return com_dist, Ra, Dec
# Convert coordinates to spherical coordinates
com_dist,Ra,Dec = coord_transform(X,Y,Z);
# Now compute RR counts
from Corrfunc.mocks.DDsmu_mocks import DDsmu_mocks
print('Computing unweighted RR pair counts')
RR=DDsmu_mocks(1,2,nthreads,mu_max,nmu_bins,binfile,Ra,Dec,com_dist,weights1=W,weight_type='pair_product',
verbose=False,is_comoving_dist=True)
# Weight by average particle weighting
RR_counts=(RR[:]['npairs']*RR[:]['weightavg']).reshape((nrbins,nmu_bins))
# Now compute ideal model for RR Counts
print("Compute correction function model")
mu_cen = np.arange(1/(2*nmu_bins),1.+1/(2*nmu_bins),1/nmu_bins)
delta_mu = (mu_cen[-1]-mu_cen[-2])
delta_mu_all = delta_mu*np.ones_like(mu_cen).reshape(1,-1)
norm = np.sum(W)**2. # partial normalization - divided by np.sum(W_gal)**2 in reconstruction script
RR_model = 4.*np.pi*(r_hi**3.-r_lo**3.).reshape(-1,1)*delta_mu_all*norm/3.
# Compute inverse Phi function and multipoles
inv_phi = RR_counts/RR_model
l_max = 4
from scipy.special import legendre
inv_Phi_multipoles = np.zeros([l_max//2+1,len(inv_phi)])
for i in range(len(RR_counts)):
for l_i,ell in enumerate(np.arange(0,l_max+2,2)):
inv_Phi_multipoles[l_i,i]=(2.*ell+1.)*delta_mu*np.sum(legendre(ell)(mu_cen)*inv_phi[i,:])
# Now fit to a smooth model
def inv_phi_ell_model(r,*par):
return par[0]+par[1]*r+par[2]*r**2.
from scipy.optimize import curve_fit
all_ell = np.arange(0,l_max+2,2)
coeff = np.asarray([curve_fit(inv_phi_ell_model,r_cen[1:],inv_Phi_multipoles[ell//2][1:],
p0=[0 for _ in range(3)])[0] for ell in all_ell])
np.savetxt(outfile,coeff,delimiter="\t")
print("Saved correction function to %s"%outfile)
|
oliverphilcoxREPO_NAMEHIPSTERPATH_START.@HIPSTER_extracted@HIPSTER-master@python@compute_correction_function.py@.PATH_END.py
|
{
"filename": "printfuncs.py",
"repo_name": "IvS-KULeuven/IvSPythonRepository",
"repo_path": "IvSPythonRepository_extracted/IvSPythonRepository-master/sigproc/lmfit/printfuncs.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 20 19:24:21 2012
@author: Tillsten
Changes:
- 13-Feb-2013 M Newville
complemented "report_errors" and "report_ci" with
"error_report" and "ci_report" (respectively) which
return the text of the report. Thus report_errors()
is simply:
def report_errors(params, modelpars=None, show_correl=True):
print error_report(params, modelpars=modelpars,
show_correl=show_correl)
and similar for report_ci() / ci_report()
"""
def fit_report(params, modelpars=None, show_correl=True, min_correl=0.1):
"""return text of a report for fitted params best-fit values,
uncertainties and correlations
arguments
----------
params Parameters from fit
modelpars Optional Known Model Parameters [None]
show_correl whether to show list of sorted correlations [True]
min_correl smallest correlation absolute value to show [0.1]
"""
parnames = sorted(params)
buff = []
add = buff.append
namelen = max([len(n) for n in parnames])
add("[[Variables]]")
for name in parnames:
par = params[name]
space = ' '*(namelen+2 - len(name))
nout = " %s: %s" % (name, space)
initval = 'inital = ?'
if par.init_value is not None:
initval = 'initial = % .6f' % par.init_value
if modelpars is not None and name in modelpars:
initval = '%s, model_value =% .6f' % (initval, modelpars[name].value)
try:
sval = '% .6f' % par.value
except (TypeError, ValueError):
sval = 'Non Numeric Value?'
if par.stderr is not None:
sval = '% .6f +/- %.6f' % (par.value, par.stderr)
try:
sval = '%s (%.2f%%)' % (sval, abs(par.stderr/par.value)*100)
except ZeroDivisionError:
pass
if par.vary:
add(" %s %s %s" % (nout, sval, initval))
elif par.expr is not None:
add(" %s %s == '%s'" % (nout, sval, par.expr))
else:
add(" %s fixed" % (nout))
if show_correl:
add('[[Correlations]] (unreported correlations are < % .3f)' % min_correl)
correls = {}
for i, name in enumerate(parnames):
par = params[name]
if not par.vary:
continue
if hasattr(par, 'correl') and par.correl is not None:
for name2 in parnames[i+1:]:
if name != name2 and name2 in par.correl:
correls["%s, %s" % (name, name2)] = par.correl[name2]
sort_correl = sorted(list(correls.items()), key=lambda it: abs(it[1]))
sort_correl.reverse()
for name, val in sort_correl:
if abs(val) < min_correl:
break
lspace = max(1, 25 - len(name))
add(' C(%s)%s = % .3f ' % (name, (' '*30)[:lspace], val))
return '\n'.join(buff)
def report_errors(params, **kws):
"""print a report for fitted params: see error_report()"""
print(fit_report(params, **kws))
def report_fit(params, **kws):
"""print a report for fitted params: see error_report()"""
print(fit_report(params, **kws))
def ci_report(ci):
"""return text of a report for confidence intervals"""
maxlen = max([len(i) for i in ci])
buff = []
add = buff.append
convp = lambda x: ("%.2f" % (x[0]*100))+'%'
conv = lambda x: "%.5f" % x[1]
title_shown = False
for name, row in list(ci.items()):
if not title_shown:
add("".join([''.rjust(maxlen)]+[i.rjust(10) for i in map(convp, row)]))
title_shown = True
add("".join([name.rjust(maxlen)]+[i.rjust(10) for i in map(conv, row)]))
return '\n'.join(buff)
def report_ci(ci):
"""print a report for confidence intervals"""
print(ci_report(ci))
|
IvS-KULeuvenREPO_NAMEIvSPythonRepositoryPATH_START.@IvSPythonRepository_extracted@IvSPythonRepository-master@sigproc@lmfit@printfuncs.py@.PATH_END.py
|
{
"filename": "plot_fit_lineshape.py",
"repo_name": "radis/radis",
"repo_path": "radis_extracted/radis-master/examples/2_Experimental_spectra/plot_fit_lineshape.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
==================================
Fit Multiple Voigt Lineshapes
==================================
Direct-access functions to fit a sum of three Voigt lineshapes on an
experimental spectrum.
This uses the underlying fitting routines of :py:mod:`specutils`
:py:func:`specutils.fitting.fit_lines` routine and some :py:mod:`astropy`
models, among :py:class:`astropy.modeling.functional_models.Gaussian1D`,
:py:class:`astropy.modeling.functional_models.Lorentz1D` or :py:class:`astropy.modeling.functional_models.Voigt1D`
"""
import numpy as np
from radis import Spectrum, calc_spectrum
from radis.test.utils import getTestFile
real_experiment = False
if real_experiment:
T_ref = 7515 # for index 9
# Using a real experiment (CO in argon) from Minesi et al. (2022) - doi:10.1007/s00340-022-07931-7
# A warning will be raised because the wavenumber are not evenly spaced (slightly)
s = Spectrum.from_mat(
getTestFile("trimmed_1857_VoigtCO_Minesi.mat"),
"absorbance",
wunit="cm-1",
unit="",
index=9,
)
else:
# If using a generated experimental spectrum
T_ref = 7000
s = calc_spectrum(
2010.6,
2011.6, # cm-1
molecule="CO",
pressure=1, # bar
Tgas=T_ref,
mole_fraction=1,
path_length=4,
wstep=0.001,
databank="hitemp",
verbose=False,
)
w, A = s.get("absorbance") # extract the wavenumber and absorbance
noise_amplitude = 5e-2
rng1 = np.random.default_rng(
122807528840384100672342137672332424406
) # to make sure the fit provides the same output each time
noise = (
noise_amplitude * rng1.random(np.size(A)) - noise_amplitude / 2
) # simulates the noise of an experiment
s = Spectrum.from_array(
w,
A + noise,
"absorbance",
wunit="cm-1",
unit="",
)
# %% Fit 3 Voigts profiles :
from astropy.modeling import models
list_models = [models.Voigt1D() for _ in range(3)]
verbose = False # verbose=True also recommended
gfit, y_err = s.fit_model(
list_models, confidence=0.9545, plot=True, verbose=verbose, debug=False
)
if verbose:
for mod in gfit:
print(mod)
print(mod.area)
# Sort models in ascending order of x0
gfit.sort(key=lambda x: x.x_0)
print("-----***********-----\nTemperature fitting:")
#%% Get temperature from line ratio - neglecting stimulated emission
from math import log
E = np.array([17475.8605, 8518.1915, 3378.9537])
S0 = np.array([2.508e-054, 3.206e-036, 3.266e-025])
nu = np.array([2010.746786, 2011.091023, 2011.421043])
name = ["R(8,24)", "R(4,7)", "P(1,25)"]
hc_k = 1.4387752
i0 = 2
T0 = 296
print("-----\nNeglecting stimulated emission:")
for index in [0, 1]:
R = 1 / (gfit[index].area / gfit[i0].area)
step = hc_k * (E[index] - E[i0])
step2 = step / T0
temp_ratio = step / (
log(R) + log(S0[index] / S0[i0]) + step2
) # see Goldenstein et al. (2016), Eq. 6
print(
"Line pair: {0}/{1} \t T = {2:.0f} K, fitting error of {3:.0f}%".format(
name[index], name[i0], temp_ratio, temp_ratio / T_ref * 100 - 100
)
)
# %% Get temperature from line ratio - accounting for stimulated emission
# Load HAPI
from radis.db.classes import get_molecule_identifier
from radis.levels.partfunc import PartFuncHAPI
M = get_molecule_identifier("CO")
isotope = 1
Q_HAPI = PartFuncHAPI(M, isotope)
###
T_K = np.arange(100, 8999, 10)
S = np.zeros((3, np.size(T_K)))
for index in [0, 1, 2]:
num_exp = 1 - np.exp(-hc_k * nu[index] / T_K)
den_exp = 1 - np.exp(-hc_k * nu[index] / T0)
S[index] = (
S0[index]
* Q_HAPI.at(T0)
/ Q_HAPI.at(list(T_K))
* np.exp(-hc_k * E[index] * (1 / T_K - 1 / T0))
* num_exp
/ den_exp
)
print("-----\nAccounting for stimulated emission:")
for index in [0, 1]:
R_calc = S[index] / S[i0]
R_meas = gfit[index].area / gfit[i0].area
temp_interp = np.interp(R_meas, R_calc, T_K)
print(
"Line pair: {0}/{1} \t T = {2:.0f} K, error of {3:.0f}%".format(
name[index], name[i0], temp_interp, temp_interp / T_ref * 100 - 100
)
)
msg = """
**Result**: this fitting routine and the R(8,24)/(P(1,25) line pair
are appropriate for temperature measurement. The R(4,7)/(P(1,25)
line pair requires a more sophiscated fitting routine, due to the
underlying transition at 2011 cm-1 from R(10,115), see Minesi et al. (2022)
"""
print(msg)
# %% Linestrength vs temperature
# import matplotlib.pyplot as plt
# for index in [0, 1, 2]:
# plt.semilogy(T_K, S[index], label=str(nu[index]))
# plt.ylim(bottom=1e-24, top=1e-19)
# plt.xlim(left=2000, right = 9100)
# plt.legend()
|
radisREPO_NAMEradisPATH_START.@radis_extracted@radis-master@examples@2_Experimental_spectra@plot_fit_lineshape.py@.PATH_END.py
|
{
"filename": "main.py",
"repo_name": "cdslaborg/paramonte",
"repo_path": "paramonte_extracted/paramonte-main/example/fortran/pm_mathGammaAM/getGammaIncUppAM/main.py",
"type": "Python"
}
|
#!/usr/bin/env python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import glob
import sys
fontsize = 17
kind = "RK"
label = [ r"shape: $\kappa = 1.0$"
, r"shape: $\kappa = 2.5$"
, r"shape: $\kappa = 5.0$"
]
pattern = "*." + kind + ".txt"
fileList = glob.glob(pattern)
if len(fileList) == 1:
df = pd.read_csv(fileList[0], delimiter = " ")
fig = plt.figure(figsize = 1.25 * np.array([6.4, 4.8]), dpi = 200)
ax = plt.subplot()
for i in range(1,len(df.values[0,:]+1)):
plt.plot( df.values[:, 0]
, df.values[:,i]
, linewidth = 2
)
plt.xticks(fontsize = fontsize - 2)
plt.yticks(fontsize = fontsize - 2)
ax.set_xlabel("x", fontsize = fontsize)
ax.set_ylabel("Regularized Upper\nIncomplete Gamma Function", fontsize = fontsize)
plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
ax.tick_params(axis = "y", which = "minor")
ax.tick_params(axis = "x", which = "minor")
ax.legend ( label
, fontsize = fontsize
#, loc = "center left"
#, bbox_to_anchor = (1, 0.5)
)
plt.savefig(fileList[0].replace(".txt",".png"))
else:
sys.exit("Ambiguous file list exists.")
|
cdslaborgREPO_NAMEparamontePATH_START.@paramonte_extracted@paramonte-main@example@fortran@pm_mathGammaAM@getGammaIncUppAM@main.py@.PATH_END.py
|
{
"filename": "AIPSData.py",
"repo_name": "bill-cotton/Obit",
"repo_path": "Obit_extracted/Obit-master/ObitSystem/ObitTalk/python/Wizardry/AIPSData.py",
"type": "Python"
}
|
# Copyright (C) 2005 Joint Institute for VLBI in Europe
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
import Obit
import OErr, OSystem
import History, UV, InfoList
# Fail gracefully if numarray isn't available.
try:
import numarray
except:
numarray = None
pass
from AIPS import AIPS
def _scalarize(value):
"""Scalarize a value.
If VALUE is a list that consists of a single element, return that
element. Otherwise return VALUE."""
if type(value) == list and len(value) == 1:
return value[0]
return value
def _vectorize(value):
"""Vectorize a value.
If VALUE is a scalar, return a list consisting of that scalar.
Otherwise return VALUE."""
if type (value) != list:
return [value]
return value
class _AIPSTableRow:
"""This class is used to access rows in an extension table."""
def __init__(self, table, fields, rownum, err):
self._err = err
self._dirty = False
self._table = table
self._fields = fields
self._rownum = rownum
if self._rownum >= 0:
assert(not self._err.isErr)
self._row = self._table.ReadRow(self._rownum + 1, self._err)
if not self._row:
raise IndexError("list index out of range")
if self._err.isErr:
raise RuntimeError
pass
return
def __str__(self):
return str(self._row)
def _findattr(self, name):
"""Return the field name corresponding to attribute NAME."""
if name in self._fields:
return self._fields[name]
msg = "%s instance has no attribute '%s'" % \
(self.__class__.__name__, name)
raise AttributeError(msg)
def __getattr__(self, name):
key = self._findattr(name)
return _scalarize(self._row[key])
def __setattr__(self, name, value):
if name.startswith('_'):
self.__dict__[name] = value
return
key = self._findattr(name)
self._row[key] = _vectorize(value)
self._dirty = True
def update(self):
"""Update this row."""
if self._dirty:
assert(not self._err.isErr)
self._table.WriteRow(self._rownum + 1, self._row, self._err)
if self._err.isErr:
raise RuntimeError
self._dirty = False
pass
return
class AIPSTableRow(_AIPSTableRow):
"""This class is used as a template row for an extension table."""
def __init__(self, table):
_AIPSTableRow.__init__(self, table._table, table._columns,
-1, table._err)
header = table._table.Desc.Dict
self._row = {}
self._row['Table name'] = header['Table name']
self._row['NumFields'] = len(header['FieldName'])
desc = zip(header['FieldName'], header['type'], header['repeat'])
for field, type, repeat in desc:
if type == 2 or type == 3:
# Integer.
self._row[field] = repeat * [0]
elif type == 9 or type == 10:
# Floating-point number.
self._row[field] = repeat * [0.0]
elif type == 13:
# String.
self._row[field] = ''
else:
msg = "Unimplemented type %d for field %s" % (type, field)
raise AssertionError(msg)
continue
return
def update(self):
# A row instantiated by the This row cannot be updated.
msg = "%s instance has no attribute 'update'" % \
self.__class__.__name__
raise AttributeError(msg)
class _AIPSTableIter(_AIPSTableRow):
"""This class is used as an iterator over rows in an extension
table."""
def __init__(self, table, fields, err):
_AIPSTableRow.__init__(self, table, fields, -1, err)
def next(self):
"""Return the next row."""
self._rownum += 1
assert(not self._err.isErr)
self._row = self._table.ReadRow(self._rownum + 1, self._err)
if not self._row:
self._err.Clear()
raise StopIteration
assert(not self._err.isErr)
return self
class _AIPSTableKeywords:
def __init__(self, table, err):
self._err = err
self._table = table
return
def __getitem__(self, key):
value = InfoList.PGet(self._table.IODesc.List, key)
return _scalarize(value[4])
def __setitem__(self, key, value):
if int(value) == value:
InfoList.PAlwaysPutInt(self._table.Desc.List, key,
[1, 1, 1, 1, 1], _vectorize(value))
InfoList.PAlwaysPutInt(self._table.IODesc.List, key,
[1, 1, 1, 1, 1], _vectorize(value))
else:
raise AssertionError("not implemented")
return
class _AIPSTable:
"""This class is used to access extension tables to an AIPS UV
data set."""
def __init__(self, data, name, version):
self._err = OErr.OErr()
self._table = data.NewTable(3, 'AIPS ' + name, version, self._err)
self._table.Open(3, self._err)
if self._err.isErr:
raise self._err
header = self._table.Desc.Dict
self._columns = {}
self._keys = []
for column in header['FieldName']:
# Convert the AIPS ccolumn names into acceptable Python
# identifiers.
key = column.lower()
key = key.replace(' ', '_')
key = key.rstrip('.')
key = key.replace('.', '_')
self._columns[key] = column
self._keys.append(key)
continue
self.name = name
self.version = header['version']
return
def close(self):
"""Close this extension table.
Closing an extension table flushes any changes to the table to
disk and updates the information in the header of the data
set."""
assert(not self._err.isErr)
self._table.Close(self._err)
if self._err.isErr:
raise RuntimeError
return
# The following functions make an extension table behave as a list
# of rows.
def __getitem__(self, key):
if key < 0:
key = len(self) - key
return _AIPSTableRow(self._table, self._columns, key, self._err)
def __iter__(self):
return _AIPSTableIter(self._table, self._columns, self._err)
def __len__(self):
return self._table.Desc.Dict['nrow']
def __setitem__(self, key, row):
if key < 0:
key = len(self) - key
assert(not self._err.isErr)
self._table.WriteRow(key + 1, row._row, self._err)
if self._err.isErr:
raise RuntimeError
return
def append(self, row):
"""Append a row to this extension table."""
assert(not self._err.isErr)
self._table.WriteRow(len(self) + 1, row._row, self._err)
if self._err.isErr:
raise RuntimeError
return
def keys(self):
return [key for key in self._keys if not key == '_status']
def _keywords(self):
return _AIPSTableKeywords(self._table, self._err)
keywords = property(_keywords)
class _AIPSHistory:
def __init__(self, data):
self._err = OErr.OErr()
self._table = History.History('AIPS HI', data.List, self._err)
self._table.Open(3, self._err)
if self._err.isErr:
raise RuntimeError
def close(self):
"""Close this history table.
Closing a history table flushes any changes to the table to
disk and updates the information in the header of the data
set."""
self._table.Close(self._err)
if self._err.isErr:
raise RuntimeError
return
def __getitem__(self, key):
rec = self._table.ReadRec(key + 1, self._err)
if not rec:
raise IndexError("list index out of range")
if self._err.isErr:
raise RuntimeError
return rec
def __setitem__(self, key, rec):
msg = 'You are not allowed to rewrite history!'
raise NotImplementedError(msg)
def append(self, rec):
"""Append a record to this history table."""
assert(not self._err.isErr)
self._table.WriteRec(0, rec, self._err)
if self._err.isErr:
raise RuntimeError
return
class _AIPSVisibilityIter(object):
"""This class is used as an iterator over visibilities."""
def __init__(self, data, err):
# Give an early warning we're not going to succeed.
if not numarray:
msg = 'Numerical Python (numarray) not available'
raise NotImplementedError(msg)
self._err = err
self._data = data
self._index = -1
self._desc = self._data.Desc.Dict
self._len = self._desc['nvis']
self._first = 0
self._count = 0
self._dirty = False
self._flush = False
return
def __len__(self):
return self._len
def next(self):
self._index += 1
if self._index + self._first >= self._len:
raise StopIteration
if self._index >= self._count:
self._fill()
return self
def _fill(self):
if self._flush and self._dirty:
Obit.UVWrite(self._data.me, self._err.me)
if self._err.isErr:
raise RuntimeError
self._flush = False
self._dirty = False
pass
Obit.UVRead(self._data.me, self._err.me)
if self._err.isErr:
raise RuntimeError
shape = len(self._data.VisBuf) / 4
self._buffer = numarray.array(sequence=self._data.VisBuf,
type=numarray.Float32, shape=shape)
self._first = self._data.Desc.Dict['firstVis'] - 1
self._count = self._data.Desc.Dict['numVisBuff']
self._buffer.setshape((self._count, -1))
self._index = 0
return
def update(self):
self._flush = True
return
def _get_uvw(self):
u = self._buffer[self._index][self._desc['ilocu']]
v = self._buffer[self._index][self._desc['ilocv']]
w = self._buffer[self._index][self._desc['ilocw']]
return [u, v, w]
uvw = property(_get_uvw)
def _get_time(self):
return self._buffer[self._index][self._desc['iloct']]
time = property(_get_time)
def _get_baseline(self):
baseline = int(self._buffer[self._index][self._desc['ilocb']])
return [baseline / 256, baseline % 256]
baseline = property(_get_baseline)
def _get_source(self):
return self._buffer[self._index][self._desc['ilocsu']]
def _set_source(self, value):
self._buffer[self._index][self._desc['ilocsu']] = value
self._dirty = True
source = property(_get_source, _set_source)
def _get_inttim(self):
return self._buffer[self._index][self._desc['ilocit']]
inttim = property(_get_inttim)
def _get_weight(self):
return self._buffer[self._index][self._desc['ilocw']]
weight = property(_get_weight)
def _get_visibility(self):
visibility = self._buffer[self._index][self._desc['nrparm']:]
inaxes = self._desc['inaxes']
shape = (inaxes[self._desc['jlocif']], inaxes[self._desc['jlocf']],
inaxes[self._desc['jlocs']], inaxes[self._desc['jlocc']])
visibility.setshape(shape)
return visibility
visibility = property(_get_visibility)
class AIPSUVData:
"""This class is used to access an AIPS UV data set."""
def __init__(self, name, klass, disk, seq):
self._err = OErr.OErr()
OSystem.PSetAIPSuser(AIPS.userno)
self._data = UV.newPAUV(name, name, klass, disk, seq, True, self._err)
if self._err.isErr:
raise RuntimeError
return
def __iter__(self):
self._data.Open(3, self._err)
return _AIPSVisibilityIter(self._data, self._err)
_antennas = []
def _generate_antennas(self):
"""Generate the 'antennas' attribute."""
if not self._antennas:
antable = self.table('AN', 0)
for antenna in antable:
self._antennas.append(antenna.anname.rstrip())
continue
pass
return self._antennas
antennas = property(_generate_antennas,
doc = 'Antennas in this data set.')
_polarizations = []
def _generate_polarizations(self):
"""Generate the 'polarizations' attribute.
Returns a list of the polarizations for this data set."""
if not self._polarizations:
for stokes in self.stokes:
if len(stokes) == 2:
for polarization in stokes:
if not polarization in self._polarizations:
self._polarizations.append(polarization)
pass
continue
pass
continue
pass
return self._polarizations
polarizations = property(_generate_polarizations,
doc='Polarizations in this data set.')
_sources = []
def _generate_sources(self):
"""Generate the 'sources' attribute."""
if not self._sources:
sutable = self.table('SU', 0)
for source in sutable:
self._sources.append(source.source.rstrip())
continue
pass
return self._sources
sources = property(_generate_sources,
doc='Sources in this data set.')
_stokes = []
def _generate_stokes(self):
"""Generate the 'stokes' attribute."""
stokes_dict = {1: 'I', 2: 'Q', 3: 'U', 4: 'V',
-1: 'RR', -2: 'LL', -3: 'RL', -4: 'LR',
-5: 'XX', -6: 'YY', -7: 'XY', -8: 'YX'}
if not self._stokes:
header = self._data.Desc.Dict
jlocs = header['jlocs']
cval = header['crval'][jlocs]
for i in xrange(header['inaxes'][jlocs]):
self._stokes.append(stokes_dict[int(cval)])
cval += header['cdelt'][jlocs]
continue
pass
return self._stokes
stokes = property(_generate_stokes,
doc='Stokes parameters for this data set.')
def table(self, name, version):
"""Access an extension table attached to this UV data set.
Returns version VERSION of the extension table NAME. If
VERSION is 0, this returns the highest available version of
the requested extension table."""
return _AIPSTable(self._data, name, version)
def attach_table(self, name, version, **kwds):
"""Attach an extension table to this UV data set.
A new extension table is created if the extension table NAME
with version VERSION doesn't exist. If VERSION is 0, a new
extension table is created with a version that is one higher
than the highest available version."""
if version == 0:
version = Obit.UVGetHighVer(self._data.me, 'AIPS ' + name) + 1
header = self._data.Desc.Dict
jlocif = header['jlocif']
no_if = header['inaxes'][jlocif]
no_pol = len(self.polarizations)
data = Obit.UVCastData(self._data.me)
if name == 'AI':
Obit.TableAI(data, [version], 3, 'AIPS ' + name,
kwds['no_term'], self._err.me)
elif name == 'CL':
Obit.TableCL(data, [version], 3, 'AIPS ' + name,
no_pol, no_if, kwds['no_term'], self._err.me)
elif name == 'SN':
Obit.TableSN(data, [version], 3, 'AIPS ' + name,
no_pol, no_if, self._err.me)
else:
raise RuntimeError
if self._err.isErr:
raise RuntimeError
return _AIPSTable(self._data, name, version)
def zap_table(self, name, version):
"""Remove an extension table from this UV data set."""
assert(not self._err.isErr)
self._data.ZapTable('AIPS + name', version, self._err)
if self._err.isErr:
raise RuntimeError
return
def history(self):
return _AIPSHistory(self._data)
err = OErr.OErr()
OSystem.OSystem('AIPSData', 1, 0, -1, [], -1, [], True, False, err)
|
bill-cottonREPO_NAMEObitPATH_START.@Obit_extracted@Obit-master@ObitSystem@ObitTalk@python@Wizardry@AIPSData.py@.PATH_END.py
|
{
"filename": "stochastic.py",
"repo_name": "google/flax",
"repo_path": "flax_extracted/flax-main/flax/nnx/nn/stochastic.py",
"type": "Python"
}
|
# Copyright 2024 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import dataclasses
from collections.abc import Sequence
import jax
import jax.numpy as jnp
from jax import lax, random
from flax.nnx import rnglib
from flax.nnx.module import Module, first_from
@dataclasses.dataclass
class Dropout(Module):
"""Create a dropout layer.
To use dropout, call the :func:`train` method (or pass in
``deterministic=False`` in the constructor or during call time).
To disable dropout, call the :func:`eval` method (or pass in
``deterministic=True`` in the constructor or during call time).
Example usage::
>>> from flax import nnx
>>> import jax.numpy as jnp
>>> class MLP(nnx.Module):
... def __init__(self, rngs):
... self.linear = nnx.Linear(in_features=3, out_features=4, rngs=rngs)
... self.dropout = nnx.Dropout(0.5, rngs=rngs)
... def __call__(self, x):
... x = self.linear(x)
... x = self.dropout(x)
... return x
>>> model = MLP(rngs=nnx.Rngs(0))
>>> x = jnp.ones((1, 3))
>>> model.train() # use dropout
>>> model(x)
Array([[-0.9353421, 0. , 1.434417 , 0. ]], dtype=float32)
>>> model.eval() # don't use dropout
>>> model(x)
Array([[-0.46767104, -0.7213411 , 0.7172085 , -0.31562346]], dtype=float32)
Attributes:
rate: the dropout probability. (_not_ the keep rate!)
broadcast_dims: dimensions that will share the same dropout mask
deterministic: if false the inputs are scaled by ``1 / (1 - rate)`` and
masked, whereas if true, no mask is applied and the inputs are returned
as is.
rng_collection: the rng collection name to use when requesting an rng key.
rngs: rng key.
"""
rate: float
broadcast_dims: Sequence[int] = ()
deterministic: bool = False
rng_collection: str = 'dropout'
rngs: rnglib.Rngs | None = None
def __call__(
self,
inputs,
*,
deterministic: bool | None = None,
rngs: rnglib.Rngs | None = None,
) -> jax.Array:
"""Applies a random dropout mask to the input.
Args:
inputs: the inputs that should be randomly masked.
deterministic: if false the inputs are scaled by ``1 / (1 - rate)`` and
masked, whereas if true, no mask is applied and the inputs are returned
as is. The ``deterministic`` flag passed into the call method will take
precedence over the ``deterministic`` flag passed into the constructor.
rngs: rng key. The rng key passed into the call method will take
precedence over the rng key passed into the constructor.
Returns:
The masked inputs reweighted to preserve mean.
"""
deterministic = first_from(
deterministic,
self.deterministic,
error_msg="""No `deterministic` argument was provided to Dropout
as either a __call__ argument or class attribute""",
)
if (self.rate == 0.0) or deterministic:
return inputs
# Prevent gradient NaNs in 1.0 edge-case.
if self.rate == 1.0:
return jnp.zeros_like(inputs)
rngs = first_from(
rngs,
self.rngs,
error_msg="""`deterministic` is False, but no `rngs` argument was provided to Dropout
as either a __call__ argument or class attribute.""",
)
keep_prob = 1.0 - self.rate
rng = rngs[self.rng_collection]()
broadcast_shape = list(inputs.shape)
for dim in self.broadcast_dims:
broadcast_shape[dim] = 1
mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)
mask = jnp.broadcast_to(mask, inputs.shape)
return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))
|
googleREPO_NAMEflaxPATH_START.@flax_extracted@flax-main@flax@nnx@nn@stochastic.py@.PATH_END.py
|
{
"filename": "xml.py",
"repo_name": "paulo-herrera/PyEVTK",
"repo_path": "PyEVTK_extracted/PyEVTK-master/evtk/xml.py",
"type": "Python"
}
|
######################################################################################
# MIT License
#
# Copyright (c) 2010-2024 Paulo A. Herrera
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
######################################################################################
# **************************************
# * Simple class to generate a *
# * well-formed XML file. *
# **************************************
_DEFAUL_ENCODING = "ASCII"
class XmlWriter:
def __init__(self, filepath, addDeclaration = True):
self.stream = open(filepath, "wb")
self.openTag = False
self.current = []
if (addDeclaration): self.addDeclaration()
def addComment(self, sstr):
""" Adds (open and close) a single comment contained in string sstr.
TODO: Add a smart check for the position of the comments in the file. For now,
we rely on the caller.
"""
if self.openTag:
self.stream.write(b">")
self.openTag = False
self.stream.write(b'\n<!-- ') # new line is not strictly necessary
self.stream.write(sstr.encode(_DEFAUL_ENCODING))
self.stream.write(b' -->') # new line here is not necessary?
def close(self):
assert(not self.openTag)
self.stream.close()
def addDeclaration(self):
self.stream.write(b'<?xml version="1.0"?>')
def openElement(self, tag):
if self.openTag: self.stream.write(b">")
st = "\n<%s" % tag
self.stream.write(st.encode(_DEFAUL_ENCODING))
self.openTag = True
self.current.append(tag)
return self
def closeElement(self, tag = None):
if tag:
assert(self.current.pop() == tag)
if (self.openTag):
self.stream.write(b">")
self.openTag = False
st = "\n</%s>" % tag
self.stream.write(st.encode(_DEFAUL_ENCODING))
else:
self.stream.write(b"/>")
self.openTag = False
self.current.pop()
return self
def addText(self, text):
if (self.openTag):
self.stream.write(b">\n")
self.openTag = False
self.stream.write(text.encode(_DEFAUL_ENCODING))
return self
def addAttributes(self, **kwargs):
assert (self.openTag)
for key in kwargs:
st = ' %s="%s"'%(key, kwargs[key])
self.stream.write(st.encode(_DEFAUL_ENCODING))
return self
|
paulo-herreraREPO_NAMEPyEVTKPATH_START.@PyEVTK_extracted@PyEVTK-master@evtk@xml.py@.PATH_END.py
|
{
"filename": "droporphanmetadata.py",
"repo_name": "CMB-S4/spt3g_software",
"repo_path": "spt3g_software_extracted/spt3g_software-master/core/tests/droporphanmetadata.py",
"type": "Python"
}
|
#!/usr/bin/env python
from spt3g import core
from spt3g.core import G3FrameType
frametypes = [G3FrameType.Wiring, G3FrameType.Calibration, G3FrameType.Calibration, G3FrameType.Timepoint, G3FrameType.Observation, G3FrameType.Wiring, G3FrameType.Observation, G3FrameType.Calibration, G3FrameType.Scan, G3FrameType.Scan, G3FrameType.Wiring]
frames = []
for i, frametype in enumerate(frametypes):
f = core.G3Frame(frametype)
f['Seq'] = i
frames.append(f)
out = []
pipe = core.G3Pipeline()
sent = False
def framesource(fr):
global sent
if not sent:
sent = True
return frames
else:
return []
pipe.Add(framesource)
pipe.Add(core.DropOrphanMetadata)
def getframes(fr):
if fr.type != G3FrameType.EndProcessing and fr.type != G3FrameType.PipelineInfo:
out.append(fr)
pipe.Add(getframes)
pipe.Run()
assert(len(out) == len(frames) - 3)
def dataframes(frs):
return [f for f in frs if f.type == core.G3FrameType.Scan or f.type == core.G3FrameType.Timepoint]
assert(len(dataframes(out)) == len(dataframes(frames)))
|
CMB-S4REPO_NAMEspt3g_softwarePATH_START.@spt3g_software_extracted@spt3g_software-master@core@tests@droporphanmetadata.py@.PATH_END.py
|
{
"filename": "test_orbits.py",
"repo_name": "jobovy/galpy",
"repo_path": "galpy_extracted/galpy-main/tests/test_orbits.py",
"type": "Python"
}
|
##########################TESTS ON MULTIPLE ORBITS#############################
import astropy
import astropy.coordinates as apycoords
import astropy.units as u
import numpy
import pytest
from galpy import potential
_APY3 = astropy.__version__ > "3"
# Test Orbits initialization
def test_initialization_vxvv():
from galpy.orbit import Orbit
# 1D
vxvvs = [[1.0, 0.1], [0.1, 3.0]]
orbits = Orbit(vxvvs)
assert (
orbits.dim() == 1
), "Orbits initialization with vxvv in 1D does not work as expected"
assert (
orbits.phasedim() == 2
), "Orbits initialization with vxvv in 1D does not work as expected"
assert (
numpy.fabs(orbits.x()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 1D does not work as expected"
assert (
numpy.fabs(orbits.x()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 1D does not work as expected"
assert (
numpy.fabs(orbits.vx()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 1D does not work as expected"
assert (
numpy.fabs(orbits.vx()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv in 1D does not work as expected"
# 2D, 3 phase-D
vxvvs = [[1.0, 0.1, 1.0], [0.1, 3.0, 1.1]]
orbits = Orbit(vxvvs)
assert (
orbits.dim() == 2
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
orbits.phasedim() == 3
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[1] - 1.1) < 1e-10
), "Orbits initialization with vxvv in 2D, 3 phase-D does not work as expected"
# 2D, 4 phase-D
vxvvs = [[1.0, 0.1, 1.0, 1.5], [0.1, 3.0, 1.1, 2.0]]
orbits = Orbit(vxvvs)
assert (
orbits.dim() == 2
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
orbits.phasedim() == 4
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[1] - 1.1) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[0] - 1.5) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[1] - 2.0) < 1e-10
), "Orbits initialization with vxvv 2D, 4 phase-D does not work as expected"
# 3D, 5 phase-D
vxvvs = [[1.0, 0.1, 1.0, 0.1, -0.2], [0.1, 3.0, 1.1, -0.3, 0.4]]
orbits = Orbit(vxvvs)
assert (
orbits.dim() == 3
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
orbits.phasedim() == 5
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[1] - 1.1) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[1] + 0.3) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[0] + 0.2) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[1] - 0.4) < 1e-10
), "Orbits initialization with vxvv 3D, 5 phase-D does not work as expected"
# 3D, 6 phase-D
vxvvs = [[1.0, 0.1, 1.0, 0.1, -0.2, 1.5], [0.1, 3.0, 1.1, -0.3, 0.4, 2.0]]
orbits = Orbit(vxvvs)
assert (
orbits.dim() == 3
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
orbits.phasedim() == 6
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.R()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[1] - 1.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[1] + 0.3) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[0] + 0.2) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[1] - 0.4) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[0] - 1.5) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[1] - 2.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
return None
def test_initialization_SkyCoord():
# Only run this for astropy>3
if not _APY3:
return None
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
# Without any custom coordinate-transformation parameters
co = apycoords.SkyCoord(
ra=ras,
dec=decs,
distance=dists,
pm_ra_cosdec=pmras,
pm_dec=pmdecs,
radial_velocity=vloss,
frame="icrs",
)
orbits = Orbit(co)
assert (
orbits.dim() == 3
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
orbits.phasedim() == 6
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
for ii in range(nrand):
to = Orbit(co[ii])
assert (
numpy.fabs(orbits.R()[ii] - to.R()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[ii] - to.vR()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[ii] - to.vT()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[ii] - to.z()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[ii] - to.vz()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[ii] - to.phi()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
# Also test list of Quantities
orbits = Orbit([ras, decs, dists, pmras, pmdecs, vloss], radec=True)
for ii in range(nrand):
to = Orbit(co[ii])
assert (
numpy.fabs((orbits.R()[ii] - to.R()) / to.R()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vR()[ii] - to.vR()) / to.vR()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vT()[ii] - to.vT()) / to.vT()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.z()[ii] - to.z()) / to.z()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vz()[ii] - to.vz()) / to.vz()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(
((orbits.phi()[ii] - to.phi() + numpy.pi) % (2.0 * numpy.pi)) - numpy.pi
)
< 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
# With custom coordinate-transformation parameters
v_sun = apycoords.CartesianDifferential([-11.1, 215.0, 3.25] * u.km / u.s)
co = apycoords.SkyCoord(
ra=ras,
dec=decs,
distance=dists,
pm_ra_cosdec=pmras,
pm_dec=pmdecs,
radial_velocity=vloss,
frame="icrs",
galcen_distance=10.0 * u.kpc,
z_sun=1.0 * u.kpc,
galcen_v_sun=v_sun,
)
orbits = Orbit(co)
assert (
orbits.dim() == 3
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
orbits.phasedim() == 6
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
for ii in range(nrand):
to = Orbit(co[ii])
assert (
numpy.fabs(orbits.R()[ii] - to.R()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vR()[ii] - to.vR()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vT()[ii] - to.vT()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.z()[ii] - to.z()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.vz()[ii] - to.vz()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits.phi()[ii] - to.phi()) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
return None
def test_initialization_list_of_arrays():
# Test that initialization using a list of arrays works (see #548)
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
dists = numpy.random.uniform(size=nrand) * 10.0
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
orbits = Orbit([ras, decs, dists, pmras, pmdecs, vloss], radec=True)
for ii in range(nrand):
to = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]], radec=True
)
assert (
numpy.fabs((orbits.R()[ii] - to.R()) / to.R()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vR()[ii] - to.vR()) / to.vR()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vT()[ii] - to.vT()) / to.vT()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.z()[ii] - to.z()) / to.z()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vz()[ii] - to.vz()) / to.vz()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(
((orbits.phi()[ii] - to.phi() + numpy.pi) % (2.0 * numpy.pi)) - numpy.pi
)
< 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
# Also test with R,vR, etc. input, badly
orbits = Orbit([ras, decs, dists, pmras, pmdecs, vloss])
for ii in range(nrand):
to = Orbit([ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]])
assert (
numpy.fabs((orbits.R()[ii] - to.R()) / to.R()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vR()[ii] - to.vR()) / to.vR()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vT()[ii] - to.vT()) / to.vT()) < 1e-7
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.z()[ii] - to.z()) / to.z()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs((orbits.vz()[ii] - to.vz()) / to.vz()) < 1e-9
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(
((orbits.phi()[ii] - to.phi() + numpy.pi) % (2.0 * numpy.pi)) - numpy.pi
)
< 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
return None
def test_initialization_diffro():
# Test that supplying an array of ro values works as expected
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], ro=ros, radec=True)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
radec=True,
)
assert (
numpy.fabs((all_orbs.R()[ii] - orb.R()) / orb.R()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.vR()[ii] - orb.vR()) / orb.vR()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.vT()[ii] - orb.vT()) / orb.vT()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.z()[ii] - orb.z()) / orb.z()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.vz()[ii] - orb.vz()) / orb.vz()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs(
((all_orbs.phi()[ii] - orb.phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
# Also some observed values like ra, dec, ...
assert (
numpy.fabs((all_orbs.ra()[ii] - orb.ra()) / orb.ra()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.dec()[ii] - orb.dec()) / orb.dec()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.dist()[ii] - orb.dist()) / orb.dist()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.pmra()[ii] - orb.pmra()) / orb.pmra()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.pmdec()[ii] - orb.pmdec()) / orb.pmdec()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert (
numpy.fabs((all_orbs.vlos()[ii] - orb.vlos()) / orb.vlos()) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
return None
def test_initialization_diffzo():
# Test that supplying an array of zo values works as expected
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
zos = (-1.0 + numpy.random.uniform(size=nrand) * 2.0) * 50.0 * u.pc
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], zo=zos, radec=True)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
zo=zos[ii],
radec=True,
)
assert (
numpy.fabs((all_orbs.R()[ii] - orb.R()) / orb.R()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.vR()[ii] - orb.vR()) / orb.vR()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.vT()[ii] - orb.vT()) / orb.vT()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.z()[ii] - orb.z()) / orb.z()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.vz()[ii] - orb.vz()) / orb.vz()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs(
((all_orbs.phi()[ii] - orb.phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
# Also some observed values like ra, dec, ...
assert (
numpy.fabs((all_orbs.ra()[ii] - orb.ra()) / orb.ra()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.dec()[ii] - orb.dec()) / orb.dec()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.dist()[ii] - orb.dist()) / orb.dist()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.pmra()[ii] - orb.pmra()) / orb.pmra()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.pmdec()[ii] - orb.pmdec()) / orb.pmdec()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert (
numpy.fabs((all_orbs.vlos()[ii] - orb.vlos()) / orb.vlos()) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
return None
def test_initialization_diffvo():
# Test that supplying a single vo value works as expected
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], vo=vos, radec=True)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
vo=vos[ii],
radec=True,
)
assert (
numpy.fabs((all_orbs.R()[ii] - orb.R()) / orb.R()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.vR()[ii] - orb.vR()) / orb.vR()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.vT()[ii] - orb.vT()) / orb.vT()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.z()[ii] - orb.z()) / orb.z()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.vz()[ii] - orb.vz()) / orb.vz()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs(
((all_orbs.phi()[ii] - orb.phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-7
), "Orbits initialization with single vo does not work as expected"
# Also some observed values like ra, dec, ...
assert (
numpy.fabs((all_orbs.ra()[ii] - orb.ra()) / orb.ra()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.dec()[ii] - orb.dec()) / orb.dec()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.dist()[ii] - orb.dist()) / orb.dist()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.pmra()[ii] - orb.pmra()) / orb.pmra()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.pmdec()[ii] - orb.pmdec()) / orb.pmdec()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
assert (
numpy.fabs((all_orbs.vlos()[ii] - orb.vlos()) / orb.vlos()) < 1e-7
), "Orbits initialization with single vo does not work as expected"
return None
def test_initialization_diffsolarmotion():
# Test that supplying an array of solarmotion values works as expected
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss], solarmotion=solarmotions, radec=True
)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
solarmotion=solarmotions[:, ii],
radec=True,
)
assert (
numpy.fabs((all_orbs.R()[ii] - orb.R()) / orb.R()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.vR()[ii] - orb.vR()) / orb.vR()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.vT()[ii] - orb.vT()) / orb.vT()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.z()[ii] - orb.z()) / orb.z()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.vz()[ii] - orb.vz()) / orb.vz()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs(
((all_orbs.phi()[ii] - orb.phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
# Also some observed values like ra, dec, ...
assert (
numpy.fabs((all_orbs.ra()[ii] - orb.ra()) / orb.ra()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.dec()[ii] - orb.dec()) / orb.dec()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.dist()[ii] - orb.dist()) / orb.dist()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.pmra()[ii] - orb.pmra()) / orb.pmra()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.pmdec()[ii] - orb.pmdec()) / orb.pmdec()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert (
numpy.fabs((all_orbs.vlos()[ii] - orb.vlos()) / orb.vlos()) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
return None
def test_initialization_allsolarparams():
# Test that supplying all parameters works as expected
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
zos = (-1.0 + numpy.random.uniform(size=nrand) * 2.0) * 50.0 * u.pc
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss],
ro=ros,
zo=zos,
vo=vos,
solarmotion=solarmotions,
radec=True,
)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
zo=zos[ii],
vo=vos[ii],
solarmotion=solarmotions[:, ii],
radec=True,
)
assert (
numpy.fabs((all_orbs.R()[ii] - orb.R()) / orb.R()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.vR()[ii] - orb.vR()) / orb.vR()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.vT()[ii] - orb.vT()) / orb.vT()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.z()[ii] - orb.z()) / orb.z()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.vz()[ii] - orb.vz()) / orb.vz()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs(
((all_orbs.phi()[ii] - orb.phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-7
), "Orbits initialization with all parameters does not work as expected"
# Also some observed values like ra, dec, ...
assert (
numpy.fabs((all_orbs.ra()[ii] - orb.ra()) / orb.ra()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.dec()[ii] - orb.dec()) / orb.dec()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.dist()[ii] - orb.dist()) / orb.dist()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.pmra()[ii] - orb.pmra()) / orb.pmra()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.pmdec()[ii] - orb.pmdec()) / orb.pmdec()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
assert (
numpy.fabs((all_orbs.vlos()[ii] - orb.vlos()) / orb.vlos()) < 1e-7
), "Orbits initialization with all parameters does not work as expected"
return None
def test_initialization_diffsolarparams_shape_error():
# Test that we get the correct error when providing the wrong shape for array
# ro/zo/vo/solarmotion inputs
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 30
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand * 2) * 2.0) * u.kpc
with pytest.raises(RuntimeError) as excinfo:
Orbit([ras, decs, dists, pmras, pmdecs, vloss], ro=ros, radec=True)
assert (
excinfo.value.args[0]
== "ro must have the same shape as the input orbits for an array of orbits"
), "Orbits initialization with wrong shape for ro does not raise correct error"
zos = (-1.0 + numpy.random.uniform(size=2 * nrand) * 2.0) * 50.0 * u.pc
with pytest.raises(RuntimeError) as excinfo:
Orbit([ras, decs, dists, pmras, pmdecs, vloss], zo=zos, radec=True)
assert (
excinfo.value.args[0]
== "zo must have the same shape as the input orbits for an array of orbits"
), "Orbits initialization with wrong shape for zo does not raise correct error"
vos = (200.0 + numpy.random.uniform(size=2 * nrand) * 40.0) * u.km / u.s
with pytest.raises(RuntimeError) as excinfo:
Orbit([ras, decs, dists, pmras, pmdecs, vloss], vo=vos, radec=True)
assert (
excinfo.value.args[0]
== "vo must have the same shape as the input orbits for an array of orbits"
), "Orbits initialization with wrong shape for vo does not raise correct error"
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, 2 * nrand)) - 1.0) * 10.0 * u.km / u.s
)
with pytest.raises(RuntimeError) as excinfo:
Orbit(
[ras, decs, dists, pmras, pmdecs, vloss],
solarmotion=solarmotions,
radec=True,
)
assert (
excinfo.value.args[0]
== "solarmotion must have the shape [3,...] where the ... matches the shape of the input orbits for an array of orbits"
), "Orbits initialization with wrong shape for solarmotion does not raise correct error"
return None
# Tests that integrating Orbits agrees with integrating multiple Orbit
# instances
def test_integration_1d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [Orbit([1.0, 0.1]), Orbit([0.1, 1.0]), Orbit([-0.2, 0.3])]
orbits = Orbit(orbits_list)
# Integrate as Orbits, twice to make sure initial cond. isn't changed
orbits.integrate(
times, potential.toVerticalPotential(potential.MWPotential2014, 1.0)
)
orbits.integrate(
times, potential.toVerticalPotential(potential.MWPotential2014, 1.0)
)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(
times, potential.toVerticalPotential(potential.MWPotential2014, 1.0)
)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_2d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3]),
Orbit([1.2, -0.3, 0.7, 5.0]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits, twice to make sure initial cond. isn't changed
orbits.integrate(times, potential.MWPotential)
orbits.integrate(times, potential.MWPotential)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].y(times) - orbits.y(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vy(times) - orbits.vy(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
(orbits_list[ii].phi(times) - orbits.phi(times)[ii] + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_p3d():
# 3D phase-space integration
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0]),
Orbit([0.9, 0.3, 1.0]),
Orbit([1.2, -0.3, 0.7]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits, twice to make sure initial cond. isn't changed
orbits.integrate(times, potential.MWPotential2014)
orbits.integrate(times, potential.MWPotential2014)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential2014)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_3d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits, twice to make sure initial cond. isn't changed
orbits.integrate(times, potential.MWPotential2014)
orbits.integrate(times, potential.MWPotential2014)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential2014)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].y(times) - orbits.y(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vy(times) - orbits.vy(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].z(times) - orbits.z(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vz(times) - orbits.vz(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
(
(orbits_list[ii].phi(times) - orbits.phi(times)[ii])
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_p5d():
# 5D phase-space integration
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits, twice to make sure initial cond. isn't changed
orbits.integrate(times, potential.MWPotential2014)
orbits.integrate(times, potential.MWPotential2014)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential2014)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].z(times) - orbits.z(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vz(times) - orbits.vz(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integrate_3d_diffro():
# Test that supplying an array of ro values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], ro=ros, radec=True)
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
radec=True,
)
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.z(times)[ii] - orb.z(times)) / orb.z(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vz(times)[ii] - orb.vz(times)) / orb.vz(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
return None
def test_integrate_3d_diffzo():
# Test that supplying an array of zo values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
zos = (-1.0 + numpy.random.uniform(size=nrand) * 2.0) * 100.0 * u.pc
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], zo=zos, radec=True)
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
zo=zos[ii],
radec=True,
)
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.z(times)[ii] - orb.z(times)) / orb.z(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vz(times)[ii] - orb.vz(times)) / orb.vz(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of zo does not work as expected"
return None
def test_integrate_3d_diffvo():
# Test that supplying an array of zo values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
all_orbs = Orbit([ras, decs, dists, pmras, pmdecs, vloss], vo=vos, radec=True)
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
vo=vos[ii],
radec=True,
)
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.z(times)[ii] - orb.z(times)) / orb.z(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vz(times)[ii] - orb.vz(times)) / orb.vz(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
return None
def test_integrate_3d_diffsolarmotion():
# Test that supplying an array of zo values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss], solarmotion=solarmotions, radec=True
)
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
solarmotion=solarmotions[:, ii],
radec=True,
)
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.z(times)[ii] - orb.z(times)) / orb.z(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vz(times)[ii] - orb.vz(times)) / orb.vz(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
return None
def test_integrate_3d_diffallsolarparams():
# Test that supplying an array of solar values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
zos = (-1.0 + numpy.random.uniform(size=nrand) * 2.0) * 100.0 * u.pc
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss],
ro=ros,
zo=zos,
vo=vos,
solarmotion=solarmotions,
radec=True,
)
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
zo=zos[ii],
vo=vos[ii],
solarmotion=solarmotions[:, ii],
radec=True,
)
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.z(times)[ii] - orb.z(times)) / orb.z(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vz(times)[ii] - orb.vz(times)) / orb.vz(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
return None
def test_integrate_2d_diffro():
# Test that supplying an array of ro values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss], ro=ros, radec=True
).toPlanar()
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
radec=True,
).toPlanar()
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of ro does not work as expected"
return None
def test_integrate_2d_diffvo():
# Test that supplying an array of zo values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss], vo=vos, radec=True
).toPlanar()
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
vo=vos[ii],
radec=True,
).toPlanar()
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of vo does not work as expected"
return None
def test_integrate_2d_diffsolarmotion():
# Test that supplying an array of zo values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss], solarmotion=solarmotions, radec=True
).toPlanar()
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
solarmotion=solarmotions[:, ii],
radec=True,
).toPlanar()
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
return None
def test_integrate_2d_diffallsolarparams():
# Test that supplying an array of solar values works as expected when integrating an orbit
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 4
ras = numpy.random.uniform(size=nrand) * 360.0 * u.deg
decs = 90.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.deg
dists = numpy.random.uniform(size=nrand) * 10.0 * u.kpc
pmras = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
pmdecs = 20.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * 20.0 * u.mas / u.yr
vloss = 200.0 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) * u.km / u.s
ros = (6.0 + numpy.random.uniform(size=nrand) * 2.0) * u.kpc
zos = (-1.0 + numpy.random.uniform(size=nrand) * 2.0) * 100.0 * u.pc
vos = (200.0 + numpy.random.uniform(size=nrand) * 40.0) * u.km / u.s
solarmotions = (
(2.0 * numpy.random.uniform(size=(3, nrand)) - 1.0) * 10.0 * u.km / u.s
)
all_orbs = Orbit(
[ras, decs, dists, pmras, pmdecs, vloss],
ro=ros,
zo=zos,
vo=vos,
solarmotion=solarmotions,
radec=True,
).toPlanar()
times = numpy.linspace(0.0, 10.0, 1001)
all_orbs.integrate(times, potential.MWPotential2014)
for ii in range(nrand):
orb = Orbit(
[ras[ii], decs[ii], dists[ii], pmras[ii], pmdecs[ii], vloss[ii]],
ro=ros[ii],
zo=zos[ii],
vo=vos[ii],
solarmotion=solarmotions[:, ii],
radec=True,
).toPlanar()
orb.integrate(times, potential.MWPotential2014)
assert numpy.all(
numpy.fabs((all_orbs.R(times)[ii] - orb.R(times)) / orb.R(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vR(times)[ii] - orb.vR(times)) / orb.vR(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vT(times)[ii] - orb.vT(times)) / orb.vT(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(
(all_orbs.phi(times)[ii] - orb.phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
# Also some observed values like ra, dec, ...
assert numpy.all(
numpy.fabs((all_orbs.ra(times)[ii] - orb.ra(times)) / orb.ra(times)) < 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dec(times)[ii] - orb.dec(times)) / orb.dec(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.dist(times)[ii] - orb.dist(times)) / orb.dist(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.pmra(times)[ii] - orb.pmra(times)) / orb.pmra(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs(
(all_orbs.pmdec(times)[ii] - orb.pmdec(times)) / orb.pmdec(times)
)
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
assert numpy.all(
numpy.fabs((all_orbs.vlos(times)[ii] - orb.vlos(times)) / orb.vlos(times))
< 1e-7
), "Orbits initialization with array of solarmotion does not work as expected"
return None
# Tests that integrating Orbits agrees with integrating multiple Orbit
# instances when using parallel_map Python parallelization
def test_integration_forcemap_1d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [Orbit([1.0, 0.1]), Orbit([0.1, 1.0]), Orbit([-0.2, 0.3])]
orbits = Orbit(orbits_list)
# Integrate as Orbits
orbits.integrate(
times,
potential.toVerticalPotential(potential.MWPotential2014, 1.0),
force_map=True,
)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(
times, potential.toVerticalPotential(potential.MWPotential2014, 1.0)
)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_forcemap_2d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3]),
Orbit([1.2, -0.3, 0.7, 5.0]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits
orbits.integrate(times, potential.MWPotential2014, force_map=True)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential2014)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].y(times) - orbits.y(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vy(times) - orbits.vy(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
(
(orbits_list[ii].phi(times) - orbits.phi(times)[ii])
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_forcemap_3d():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits
orbits.integrate(times, potential.MWPotential2014, force_map=True)
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, potential.MWPotential2014)
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].x(times) - orbits.x(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vx(times) - orbits.vx(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].y(times) - orbits.y(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vy(times) - orbits.vy(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].z(times) - orbits.z(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vz(times) - orbits.vz(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
(
(orbits_list[ii].phi(times) - orbits.phi(times)[ii])
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integration_dxdv_2d():
from galpy.orbit import Orbit
lp = potential.LogarithmicHaloPotential(normalize=1.0)
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3]),
Orbit([1.2, -0.3, 0.7, 5.0]),
]
orbits = Orbit(orbits_list)
numpy.random.seed(1)
dxdv = (2.0 * numpy.random.uniform(size=orbits.shape + (4,)) - 1) / 10.0
# Default, C integration
integrator = "dopr54_c"
orbits.integrate_dxdv(dxdv, times, lp, method=integrator)
# Integrate as multiple Orbits
for o, tdxdv in zip(orbits_list, dxdv):
o.integrate_dxdv(tdxdv, times, lp, method=integrator)
assert (
numpy.amax(
numpy.fabs(
orbits.getOrbit_dxdv()
- numpy.array([o.getOrbit_dxdv() for o in orbits_list])
)
)
< 1e-8
), "Integration of the phase-space volume of multiple orbits as Orbits does not agree with integrating the phase-space volume of multiple orbits"
# Python integration
integrator = "odeint"
orbits.integrate_dxdv(dxdv, times, lp, method=integrator)
# Integrate as multiple Orbits
for o, tdxdv in zip(orbits_list, dxdv):
o.integrate_dxdv(tdxdv, times, lp, method=integrator)
assert (
numpy.amax(
numpy.fabs(
orbits.getOrbit_dxdv()
- numpy.array([o.getOrbit_dxdv() for o in orbits_list])
)
)
< 1e-8
), "Integration of the phase-space volume of multiple orbits as Orbits does not agree with integrating the phase-space volume of multiple orbits"
return None
def test_integration_dxdv_2d_rectInOut():
from galpy.orbit import Orbit
lp = potential.LogarithmicHaloPotential(normalize=1.0)
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3]),
Orbit([1.2, -0.3, 0.7, 5.0]),
]
orbits = Orbit(orbits_list)
numpy.random.seed(1)
dxdv = (2.0 * numpy.random.uniform(size=orbits.shape + (4,)) - 1) / 10.0
# Default, C integration
integrator = "dopr54_c"
orbits.integrate_dxdv(dxdv, times, lp, method=integrator, rectIn=True, rectOut=True)
# Integrate as multiple Orbits
for o, tdxdv in zip(orbits_list, dxdv):
o.integrate_dxdv(tdxdv, times, lp, method=integrator, rectIn=True, rectOut=True)
assert (
numpy.amax(
numpy.fabs(
orbits.getOrbit_dxdv()
- numpy.array([o.getOrbit_dxdv() for o in orbits_list])
)
)
< 1e-8
), "Integration of the phase-space volume of multiple orbits as Orbits does not agree with integrating the phase-space volume of multiple orbits"
# Python integration
integrator = "odeint"
orbits.integrate_dxdv(dxdv, times, lp, method=integrator, rectIn=True, rectOut=True)
# Integrate as multiple Orbits
for o, tdxdv in zip(orbits_list, dxdv):
o.integrate_dxdv(tdxdv, times, lp, method=integrator, rectIn=True, rectOut=True)
assert (
numpy.amax(
numpy.fabs(
orbits.getOrbit_dxdv()
- numpy.array([o.getOrbit_dxdv() for o in orbits_list])
)
)
< 1e-8
), "Integration of the phase-space volume of multiple orbits as Orbits does not agree with integrating the phase-space volume of multiple orbits"
return None
# Test that the 3D SOS function returns points with z=0, vz > 0
def test_SOS_3D():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.MWPotential2014
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.SOS(
pot,
method=method,
ncross=500 if "_c" in method else 20,
force_map="rk" in method,
t0=numpy.arange(len(orbits)),
)
zs = orbits.z(orbits.t)
vzs = orbits.vz(orbits.t)
assert (
numpy.fabs(zs) < 10.0**-6.0
).all(), f"z on SOS is not zero for integrate_sos for method={method}"
assert (
vzs > 0.0
).all(), f"vz on SOS is not positive for integrate_sos for method={method}"
return None
# Test that the 2D SOS function returns points with x=0, vx > 0
def test_SOS_2Dx():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, 3.0]),
Orbit([1.2, -0.3, 0.7, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.LogarithmicHaloPotential(normalize=1.0, q=0.9).toPlanar()
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.SOS(
pot,
method=method,
ncross=500 if "_c" in method else 20,
force_map="rk" in method,
t0=numpy.arange(len(orbits)),
surface="x",
)
xs = orbits.x(orbits.t)
vxs = orbits.vx(orbits.t)
assert (
numpy.fabs(xs) < 10.0**-6.0
).all(), f"x on SOS is not zero for integrate_sos for method={method}"
assert (
vxs > 0.0
).all(), f"vx on SOS is not positive for integrate_sos for method={method}"
return None
# Test that the 2D SOS function returns points with y=0, vy > 0
def test_SOS_2Dy():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, 3.0]),
Orbit([1.2, -0.3, 0.7, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.LogarithmicHaloPotential(normalize=1.0, q=0.9).toPlanar()
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.SOS(
pot,
method=method,
ncross=500 if "_c" in method else 20,
force_map="rk" in method,
t0=numpy.arange(len(orbits)),
surface="y",
)
ys = orbits.y(orbits.t)
vys = orbits.vy(orbits.t)
assert (
numpy.fabs(ys) < 10.0**-6.0
).all(), f"y on SOS is not zero for integrate_sos for method={method}"
assert (
vys > 0.0
).all(), f"vy on SOS is not positive for integrate_sos for method={method}"
return None
# Test that the SOS integration returns an error
# when one orbit does not leave the surface
def test_SOS_onsurfaceerror_3D():
from galpy.orbit import Orbit
o = Orbit([[1.0, 0.1, 1.1, 0.1, 0.0, 0.0], [1.0, 0.1, 1.1, 0.0, 0.0, 0.0]])
with pytest.raises(
RuntimeError,
match="An orbit appears to be within the SOS surface. Refusing to perform specialized SOS integration, please use normal integration instead",
):
o.SOS(potential.MWPotential2014)
return None
# Test that the 3D bruteSOS function returns points with z=0, vz > 0
def test_bruteSOS_3D():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.MWPotential2014
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.bruteSOS(
numpy.linspace(0.0, 20.0 * numpy.pi, 100001),
pot,
method=method,
force_map="rk" in method,
)
zs = orbits.z(orbits.t)
vzs = orbits.vz(orbits.t)
assert (
numpy.fabs(zs[~numpy.isnan(zs)]) < 10.0**-3.0
).all(), f"z on bruteSOS is not zero for bruteSOS for method={method}"
assert (
vzs[~numpy.isnan(zs)] > 0.0
).all(), f"vz on bruteSOS is not positive for bruteSOS for method={method}"
return None
# Test that the 2D bruteSOS function returns points with x=0, vx > 0
def test_bruteSOS_2Dx():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, 3.0]),
Orbit([1.2, -0.3, 0.7, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.LogarithmicHaloPotential(normalize=1.0, q=0.9).toPlanar()
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.bruteSOS(
numpy.linspace(0.0, 20.0 * numpy.pi, 100001),
pot,
method=method,
force_map="rk" in method,
surface="x",
)
xs = orbits.x(orbits.t)
vxs = orbits.vx(orbits.t)
assert (
numpy.fabs(xs[~numpy.isnan(xs)]) < 10.0**-3.0
).all(), f"x on SOS is not zero for bruteSOS for method={method}"
assert (
vxs[~numpy.isnan(xs)] > 0.0
).all(), f"vx on SOS is not zero for bruteSOS for method={method}"
return None
# Test that the 2D bruteSOS function returns points with y=0, vy > 0
def test_bruteSOS_2Dy():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0]),
Orbit([0.9, 0.3, 1.0, 3.0]),
Orbit([1.2, -0.3, 0.7, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.LogarithmicHaloPotential(normalize=1.0, q=0.9).toPlanar()
for method in ["dopr54_c", "dop853_c", "rk4_c", "rk6_c", "dop853", "odeint"]:
orbits.bruteSOS(
numpy.linspace(0.0, 20.0 * numpy.pi, 100001),
pot,
method=method,
force_map="rk" in method,
surface="y",
)
ys = orbits.y(orbits.t)
vys = orbits.vy(orbits.t)
assert (
numpy.fabs(ys[~numpy.isnan(ys)]) < 10.0**-3.0
).all(), f"y on SOS is not zero for bruteSOS for method={method}"
assert (
vys[~numpy.isnan(ys)] > 0.0
).all(), f"vy on SOS is not zero for bruteSOS for method={method}"
return None
# Test slicing of orbits
def test_slice_singleobject():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
orbits.integrate(times, potential.MWPotential2014)
indices = [0, 1, -1]
for ii in indices:
assert (
numpy.amax(numpy.fabs(orbits[ii].x(times) - orbits.x(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vx(times) - orbits.vx(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].y(times) - orbits.y(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vy(times) - orbits.vy(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].z(times) - orbits.z(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vz(times) - orbits.vz(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].R(times) - orbits.R(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vR(times) - orbits.vR(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vT(times) - orbits.vT(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
((orbits[ii].phi(times) - orbits.phi(times)[ii]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
# Test slicing of orbits
def test_slice_multipleobjects():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
Orbit([0.6, -0.4, 0.4, 0.25, -0.5, 6.0]),
Orbit([1.1, -0.13, 0.17, 0.35, -0.5, 2.0]),
]
orbits = Orbit(orbits_list)
# Pre-integration
orbits_slice = orbits[1:4]
for ii in range(3):
assert (
numpy.amax(numpy.fabs(orbits_slice.x()[ii] - orbits.x()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.vx()[ii] - orbits.vx()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.y()[ii] - orbits.y()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.vy()[ii] - orbits.vy()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.z()[ii] - orbits.z()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.vz()[ii] - orbits.vz()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.R()[ii] - orbits.R()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.vR()[ii] - orbits.vR()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.vT()[ii] - orbits.vT()[ii + 1])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.phi()[ii] - orbits.phi()[ii + 1]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
# After integration
orbits.integrate(times, potential.MWPotential2014)
orbits_slice = orbits[1:4]
for ii in range(3):
assert (
numpy.amax(numpy.fabs(orbits_slice.x(times)[ii] - orbits.x(times)[ii + 1]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.vx(times)[ii] - orbits.vx(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.y(times)[ii] - orbits.y(times)[ii + 1]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.vy(times)[ii] - orbits.vy(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.z(times)[ii] - orbits.z(times)[ii + 1]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.vz(times)[ii] - orbits.vz(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_slice.R(times)[ii] - orbits.R(times)[ii + 1]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.vR(times)[ii] - orbits.vR(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.vT(times)[ii] - orbits.vT(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(orbits_slice.phi(times)[ii] - orbits.phi(times)[ii + 1])
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
# Test slicing of orbits with non-trivial shapes
def test_slice_singleobject_multidim():
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = (5, 1, 3)
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vxvv = numpy.rollaxis(numpy.array([Rs, vRs, vTs, zs, vzs, phis]), 0, 4)
orbits = Orbit(vxvv)
times = numpy.linspace(0.0, 10.0, 1001)
orbits.integrate(times, potential.MWPotential2014)
indices = [(0, 0, 0), (1, 0, 2), (-1, 0, 1)]
for ii in indices:
assert (
numpy.amax(numpy.fabs(orbits[ii].x(times) - orbits.x(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vx(times) - orbits.vx(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].y(times) - orbits.y(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vy(times) - orbits.vy(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].z(times) - orbits.z(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vz(times) - orbits.vz(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].R(times) - orbits.R(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vR(times) - orbits.vR(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vT(times) - orbits.vT(times)[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
((orbits[ii].phi(times) - orbits.phi(times)[ii]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
# Test slicing of orbits with non-trivial shapes
def test_slice_multipleobjects_multidim():
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = (5, 1, 3)
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vxvv = numpy.rollaxis(numpy.array([Rs, vRs, vTs, zs, vzs, phis]), 0, 4)
orbits = Orbit(vxvv)
times = numpy.linspace(0.0, 10.0, 1001)
# Pre-integration
orbits_slice = orbits[1:4, 0, :2]
for ii in range(3):
for jj in range(1):
for kk in range(2):
assert (
numpy.amax(
numpy.fabs(
orbits_slice.x()[ii, kk] - orbits.x()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vx()[ii, kk] - orbits.vx()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.y()[ii, kk] - orbits.y()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vy()[ii, kk] - orbits.vy()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.z()[ii, kk] - orbits.z()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vz()[ii, kk] - orbits.vz()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.R()[ii, kk] - orbits.R()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vR()[ii, kk] - orbits.vR()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vT()[ii, kk] - orbits.vT()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.phi()[ii, kk] - orbits.phi()[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
# After integration
orbits.integrate(times, potential.MWPotential2014)
orbits_slice = orbits[1:4, 0, :2]
for ii in range(3):
for jj in range(1):
for kk in range(2):
assert (
numpy.amax(
numpy.fabs(
orbits_slice.x(times)[ii, kk]
- orbits.x(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vx(times)[ii, kk]
- orbits.vx(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.y(times)[ii, kk]
- orbits.y(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vy(times)[ii, kk]
- orbits.vy(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.z(times)[ii, kk]
- orbits.z(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vz(times)[ii, kk]
- orbits.vz(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.R(times)[ii, kk]
- orbits.R(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vR(times)[ii, kk]
- orbits.vR(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.vT(times)[ii, kk]
- orbits.vT(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
orbits_slice.phi(times)[ii, kk]
- orbits.phi(times)[ii + 1, jj, kk]
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_slice_integratedorbit_wrapperpot_367():
# Test related to issue 367: slicing orbits with a potential that includes
# a wrapper potential (from Ted Mackereth)
from galpy.orbit import Orbit
from galpy.potential import (
DehnenBarPotential,
DehnenSmoothWrapperPotential,
LogarithmicHaloPotential,
)
# initialise a wrapper potential
tform = -10.0
tsteady = 5.0
omega = 1.85
angle = 25.0 / 180.0 * numpy.pi
dp = DehnenBarPotential(
omegab=omega,
rb=3.5 / 8.0,
Af=(1.0 / 75.0),
tform=tform,
tsteady=tsteady,
barphi=angle,
)
lhp = LogarithmicHaloPotential(normalize=1.0)
dswp = DehnenSmoothWrapperPotential(
pot=dp,
tform=-4.0 * 2.0 * numpy.pi / dp.OmegaP(),
tsteady=2.0 * 2.0 * numpy.pi / dp.OmegaP(),
)
pot = [lhp, dswp]
# initialise 2 random orbits
r = numpy.random.randn(2) * 0.01 + 1.0
z = numpy.random.randn(2) * 0.01 + 0.2
phi = numpy.random.randn(2) * 0.01 + 0.0
vR = numpy.random.randn(2) * 0.01 + 0.0
vT = numpy.random.randn(2) * 0.01 + 1.0
vz = numpy.random.randn(2) * 0.01 + 0.02
vxvv = numpy.dstack([r, vR, vT, z, vz, phi])[0]
os = Orbit(vxvv)
times = numpy.linspace(0.0, 100.0, 3000)
os.integrate(times, pot)
# This failed in #367
assert (
not os[0] is None
), "Slicing an integrated Orbits instance with a WrapperPotential does not work"
return None
# Test that slicing of orbits propagates unit info
def test_slice_physical_issue385():
from galpy.orbit import Orbit
ra = [17.2875, 302.2875, 317.79583333, 306.60833333, 9.65833333, 147.2]
dec = [61.54730278, 42.86525833, 17.72774722, 9.45011944, -7.69072222, 13.74425556]
dist = [0.16753225, 0.08499065, 0.03357057, 0.05411548, 0.11946004, 0.0727802]
pmra = [633.01, 119.536, -122.216, 116.508, 20.34, 373.05]
pmdec = [65.303, 540.224, -899.263, -548.329, -546.373, -774.38]
vlos = [-317.86, -195.44, -44.15, -246.76, -46.79, -15.17]
orbits = Orbit(
numpy.column_stack([ra, dec, dist, pmra, pmdec, vlos]),
radec=True,
ro=9.0,
vo=230.0,
solarmotion=[-11.1, 24.0, 7.25],
)
assert (
orbits._roSet
), "Test Orbit instance that was supposed to have physical output turned does not"
assert (
orbits._voSet
), "Test Orbit instance that was supposed to have physical output turned does not"
assert (
numpy.fabs(orbits._ro - 9.0) < 1e-10
), "Test Orbit instance that was supposed to have physical output turned does not have the right ro"
assert (
numpy.fabs(orbits._vo - 230.0) < 1e-10
), "Test Orbit instance that was supposed to have physical output turned does not have the right vo"
for ii in range(orbits.size):
assert orbits[
ii
]._roSet, "Sliced Orbit instance that was supposed to have physical output turned does not"
assert orbits[
ii
]._voSet, "Sliced Orbit instance that was supposed to have physical output turned does not"
assert (
numpy.fabs(orbits[ii]._ro - 9.0) < 1e-10
), "Sliced Orbit instance that was supposed to have physical output turned does not have the right ro"
assert (
numpy.fabs(orbits[ii]._vo - 230.0) < 1e-10
), "Sliced Orbit instance that was supposed to have physical output turned does not have the right vo"
assert (
numpy.fabs(orbits[ii]._zo - orbits._zo) < 1e-10
), "Sliced Orbit instance that was supposed to have physical output turned does not have the right zo"
assert numpy.all(
numpy.fabs(orbits[ii]._solarmotion - orbits._solarmotion) < 1e-10
), "Sliced Orbit instance that was supposed to have physical output turned does not have the right zo"
assert (
numpy.amax(numpy.fabs(orbits[ii].x() - orbits.x()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vx() - orbits.vx()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].y() - orbits.y()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vy() - orbits.vy()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].z() - orbits.z()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vz() - orbits.vz()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].R() - orbits.R()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vR() - orbits.vR()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits[ii].vT() - orbits.vT()[ii])) < 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(
numpy.fabs(
(
((orbits[ii].phi() - orbits.phi()[ii]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
)
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
# Test that slicing in the case of individual time arrays works as expected
# Currently, the only way individual time arrays occur is through SOS integration
# so we implementing this test using SOS integration
def test_slice_indivtimes():
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.0, 0.1, 0.0]),
Orbit([0.9, 0.3, 1.0, -0.3, 0.4, 3.0]),
Orbit([1.2, -0.3, 0.7, 0.5, -0.5, 6.0]),
]
orbits = Orbit(orbits_list)
pot = potential.MWPotential2014
orbits.SOS(pot, t0=numpy.arange(len(orbits)))
# First check that we actually have individual times
assert (
len(orbits.t.shape) >= len(orbits.orbit.shape) - 1
), "Test should be using individual time arrays, but a single time array was found"
# Now slice single and multiple
assert numpy.all(
orbits[0].t == orbits.t[0]
), "Individually sliced orbit with individual time arrays does not produce the correct time array in the slice"
assert numpy.all(
orbits[1].t == orbits.t[1]
), "Individually sliced orbit with individual time arrays does not produce the correct time array in the slice"
assert numpy.all(
orbits[:2].t == orbits.t[:2]
), "Multiply-sliced orbit with individual time arrays does not produce the correct time array in the slice"
assert numpy.all(
orbits[1:4].t == orbits.t[1:4]
), "Multiply-sliced orbit with individual time arrays does not produce the correct time array in the slice"
return None
# Test that initializing Orbits with orbits with different phase-space
# dimensions raises an error
def test_initialize_diffphasedim_error():
from galpy.orbit import Orbit
# 2D with 3D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1], [1.0, 0.1, 1.0]])
# 2D with 4D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1], [1.0, 0.1, 1.0, 0.1]])
# 2D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1], [1.0, 0.1, 1.0, 0.1, 0.2]])
# 2D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1], [1.0, 0.1, 1.0, 0.1, 0.2, 3.0]])
# 3D with 4D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0], [1.0, 0.1, 1.0, 0.1]])
# 3D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0], [1.0, 0.1, 1.0, 0.1, 0.2]])
# 3D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0], [1.0, 0.1, 1.0, 0.1, 0.2, 6.0]])
# 4D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0, 2.0], [1.0, 0.1, 1.0, 0.1, 0.2]])
# 4D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0, 2.0], [1.0, 0.1, 1.0, 0.1, 0.2, 6.0]])
# 5D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([[1.0, 0.1, 1.0, 0.2, -0.2], [1.0, 0.1, 1.0, 0.1, 0.2, 6.0]])
# Also as Orbit inputs
# 2D with 3D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1]), Orbit([1.0, 0.1, 1.0])])
# 2D with 4D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1]), Orbit([1.0, 0.1, 1.0, 0.1])])
# 2D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2])])
# 2D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2, 3.0])])
# 3D with 4D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1, 1.0]), Orbit([1.0, 0.1, 1.0, 0.1])])
# 3D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1, 1.0]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2])])
# 3D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1, 1.0]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2, 6.0])])
# 4D with 5D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1, 1.0, 2.0]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2])])
# 4D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit([Orbit([1.0, 0.1, 1.0, 2.0]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2, 6.0])])
# 5D with 6D
with pytest.raises(
(RuntimeError, ValueError),
match="All individual orbits in an Orbit class must have the same phase-space dimensionality",
) as excinfo:
Orbit(
[Orbit([1.0, 0.1, 1.0, 0.2, -0.2]), Orbit([1.0, 0.1, 1.0, 0.1, 0.2, 6.0])]
)
return None
# Test that initializing Orbits with a list of non-scalar Orbits raises an error
def test_initialize_listorbits_error():
from galpy.orbit import Orbit
with pytest.raises(RuntimeError) as excinfo:
Orbit([Orbit([[1.0, 0.1], [1.0, 0.1]]), Orbit([[1.0, 0.1], [1.0, 0.1]])])
return None
# Test that initializing Orbits with an array of the wrong shape raises an error, that is, the phase-space dim part is > 6 or 1
def test_initialize_wrongshape():
from galpy.orbit import Orbit
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(2, 12)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(3, 12)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(4, 12)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(2, 1)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(5, 12)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(6, 12)))
with pytest.raises(RuntimeError) as excinfo:
Orbit(numpy.random.uniform(size=(7, 12)))
return None
def test_orbits_consistentro():
from galpy.orbit import Orbit
ro = 7.0
# Initialize Orbits from list of Orbit instances
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], ro=ro),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], ro=ro),
]
orbits = Orbit(orbits_list)
# Check that ro is taken correctly
assert (
numpy.fabs(orbits._ro - orbits_list[0]._ro) < 1e-10
), "Orbits' ro not correctly taken from input list of Orbit instances"
assert (
orbits._roSet
), "Orbits' ro not correctly taken from input list of Orbit instances"
# Check that consistency of ros is enforced
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, ro=6.0)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], ro=ro),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], ro=ro * 1.2),
]
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, ro=ro)
return None
def test_orbits_consistentvo():
from galpy.orbit import Orbit
vo = 230.0
# Initialize Orbits from list of Orbit instances
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], vo=vo),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], vo=vo),
]
orbits = Orbit(orbits_list)
# Check that vo is taken correctly
assert (
numpy.fabs(orbits._vo - orbits_list[0]._vo) < 1e-10
), "Orbits' vo not correctly taken from input list of Orbit instances"
assert (
orbits._voSet
), "Orbits' vo not correctly taken from input list of Orbit instances"
# Check that consistency of vos is enforced
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, vo=210.0)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], vo=vo),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], vo=vo * 1.2),
]
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, vo=vo)
return None
def test_orbits_consistentzo():
from galpy.orbit import Orbit
zo = 0.015
# Initialize Orbits from list of Orbit instances
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], zo=zo),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], zo=zo),
]
orbits = Orbit(orbits_list)
# Check that zo is taken correctly
assert (
numpy.fabs(orbits._zo - orbits_list[0]._zo) < 1e-10
), "Orbits' zo not correctly taken from input list of Orbit instances"
# Check that consistency of zos is enforced
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, zo=0.045)
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], zo=zo),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], zo=zo * 1.2),
]
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, zo=zo)
return None
def test_orbits_consistentsolarmotion():
from galpy.orbit import Orbit
solarmotion = numpy.array([-10.0, 20.0, 30.0])
# Initialize Orbits from list of Orbit instances
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], solarmotion=solarmotion),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], solarmotion=solarmotion),
]
orbits = Orbit(orbits_list)
# Check that solarmotion is taken correctly
assert numpy.all(
numpy.fabs(orbits._solarmotion - orbits_list[0]._solarmotion) < 1e-10
), "Orbits' solarmotion not correctly taken from input list of Orbit instances"
# Check that consistency of solarmotions is enforced
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, solarmotion=numpy.array([15.0, 20.0, 30]))
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, solarmotion=numpy.array([-10.0, 25.0, 30]))
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, solarmotion=numpy.array([-10.0, 20.0, -30]))
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], solarmotion=solarmotion),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], solarmotion=solarmotion * 1.2),
]
with pytest.raises(RuntimeError) as excinfo:
orbits = Orbit(orbits_list, solarmotion=solarmotion)
return None
def test_orbits_stringsolarmotion():
from galpy.orbit import Orbit
solarmotion = "hogg"
orbits_list = [
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -3.0], solarmotion=solarmotion),
Orbit([1.0, 0.1, 1.0, 0.1, 0.2, -4.0], solarmotion=solarmotion),
]
orbits = Orbit(orbits_list, solarmotion="hogg")
assert numpy.all(
numpy.fabs(orbits._solarmotion - numpy.array([-10.1, 4.0, 6.7])) < 1e-10
), "String solarmotion not parsed correctly"
return None
def test_orbits_dim_2dPot_3dOrb():
# Test that orbit integration throws an error when using a potential that
# is lower dimensional than the orbit (using ~Plevne's example)
from galpy.orbit import Orbit
from galpy.util import conversion
b_p = potential.PowerSphericalPotentialwCutoff(
alpha=1.8, rc=1.9 / 8.0, normalize=0.05
)
ell_p = potential.EllipticalDiskPotential()
pota = [b_p, ell_p]
o = Orbit(
[
Orbit(
vxvv=[20.0, 10.0, 2.0, 3.2, 3.4, -100.0], radec=True, ro=8.0, vo=220.0
),
Orbit(
vxvv=[20.0, 10.0, 2.0, 3.2, 3.4, -100.0], radec=True, ro=8.0, vo=220.0
),
]
)
ts = numpy.linspace(
0.0, 3.5 / conversion.time_in_Gyr(vo=220.0, ro=8.0), 1000, endpoint=True
)
with pytest.raises(AssertionError) as excinfo:
o.integrate(ts, pota, method="odeint")
return None
def test_orbit_dim_1dPot_3dOrb():
# Test that orbit integration throws an error when using a potential that
# is lower dimensional than the orbit, for a 1D potential
from galpy.orbit import Orbit
from galpy.util import conversion
b_p = potential.PowerSphericalPotentialwCutoff(
alpha=1.8, rc=1.9 / 8.0, normalize=0.05
)
pota = potential.RZToverticalPotential(b_p, 1.1)
o = Orbit(
[
Orbit(
vxvv=[20.0, 10.0, 2.0, 3.2, 3.4, -100.0], radec=True, ro=8.0, vo=220.0
),
Orbit(
vxvv=[20.0, 10.0, 2.0, 3.2, 3.4, -100.0], radec=True, ro=8.0, vo=220.0
),
]
)
ts = numpy.linspace(
0.0, 3.5 / conversion.time_in_Gyr(vo=220.0, ro=8.0), 1000, endpoint=True
)
with pytest.raises(AssertionError) as excinfo:
o.integrate(ts, pota, method="odeint")
return None
def test_orbit_dim_1dPot_2dOrb():
# Test that orbit integration throws an error when using a potential that
# is lower dimensional than the orbit, for a 1D potential
from galpy.orbit import Orbit
b_p = potential.PowerSphericalPotentialwCutoff(
alpha=1.8, rc=1.9 / 8.0, normalize=0.05
)
pota = [b_p.toVertical(1.1)]
o = Orbit([Orbit(vxvv=[1.1, 0.1, 1.1, 0.1]), Orbit(vxvv=[1.1, 0.1, 1.1, 0.1])])
ts = numpy.linspace(0.0, 10.0, 1001)
with pytest.raises(AssertionError) as excinfo:
o.integrate(ts, pota, method="leapfrog")
with pytest.raises(AssertionError) as excinfo:
o.integrate(ts, pota, method="dop853")
return None
# Test the error for when explicit stepsize does not divide the output stepsize
def test_check_integrate_dt():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
o = Orbit(
[Orbit([1.0, 0.1, 1.2, 0.3, 0.2, 2.0]), Orbit([1.0, 0.1, 1.2, 0.3, 0.2, 2.0])]
)
times = numpy.linspace(0.0, 7.0, 251)
# This shouldn't work
try:
o.integrate(times, lp, dt=(times[1] - times[0]) / 4.0 * 1.1)
except ValueError:
pass
else:
raise AssertionError(
"dt that is not an integer divisor of the output step size does not raise a ValueError"
)
# This should
try:
o.integrate(times, lp, dt=(times[1] - times[0]) / 4.0)
except ValueError:
raise AssertionError(
"dt that is an integer divisor of the output step size raises a ValueError"
)
return None
# Test that evaluating coordinate functions for integrated orbits works
def test_coordinate_interpolation():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# Before integration
for ii in range(nrand):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time() - list_os[ii].time()) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R()[ii] - list_os[ii].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii] - list_os[ii].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii] - list_os[ii].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii] - list_os[ii].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z()[ii] - list_os[ii].z()) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz()[ii] - list_os[ii].vz()) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
((os.phi()[ii] - list_os[ii].phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x()[ii] - list_os[ii].x()) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y()[ii] - list_os[ii].y()) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx()[ii] - list_os[ii].vx()) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy()[ii] - list_os[ii].vy()) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii] - list_os[ii].vphi()) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra()[ii] - list_os[ii].ra()) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec()[ii] - list_os[ii].dec()) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist()[ii] - list_os[ii].dist()) < 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll()[ii] - list_os[ii].ll()) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb()[ii] - list_os[ii].bb()) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra()[ii] - list_os[ii].pmra()) < 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec()[ii] - list_os[ii].pmdec()) < 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll()[ii] - list_os[ii].pmll()) < 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb()[ii] - list_os[ii].pmbb()) < 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra()[ii] - list_os[ii].vra()) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec()[ii] - list_os[ii].vdec()) < 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll()[ii] - list_os[ii].vll()) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb()[ii] - list_os[ii].vbb()) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos()[ii] - list_os[ii].vlos()) < 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX()[ii] - list_os[ii].helioX()) < 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY()[ii] - list_os[ii].helioY()) < 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ()[ii] - list_os[ii].helioZ()) < 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U()[ii] - list_os[ii].U()) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V()[ii] - list_os[ii].V()) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W()[ii] - list_os[ii].W()) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord().ra[ii] - list_os[ii].SkyCoord().ra).to(u.deg).value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord().dec[ii] - list_os[ii].SkyCoord().dec)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord().distance[ii] - list_os[ii].SkyCoord().distance)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord().pm_ra_cosdec[ii] - list_os[ii].SkyCoord().pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord().pm_dec[ii] - list_os[ii].SkyCoord().pm_dec)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().radial_velocity[ii]
- list_os[ii].SkyCoord().radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times) - list_os[ii].time(times)) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times)[ii] - list_os[ii].R(times)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii] - list_os[ii].r(times)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii] - list_os[ii].vR(times)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii] - list_os[ii].vT(times)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times)[ii] - list_os[ii].z(times)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times)[ii] - list_os[ii].vz(times)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(times)[ii] - list_os[ii].phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x(times)[ii] - list_os[ii].x(times)) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y(times)[ii] - list_os[ii].y(times)) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times)[ii] - list_os[ii].vx(times)) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy(times)[ii] - list_os[ii].vy(times)) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times)[ii] - list_os[ii].vphi(times)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(times)[ii] - list_os[ii].ra(times)) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec(times)[ii] - list_os[ii].dec(times)) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist(times)[ii] - list_os[ii].dist(times)) < 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(times)[ii] - list_os[ii].ll(times)) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(times)[ii] - list_os[ii].bb(times)) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra(times)[ii] - list_os[ii].pmra(times)) < 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec(times)[ii] - list_os[ii].pmdec(times)) < 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll(times)[ii] - list_os[ii].pmll(times)) < 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb(times)[ii] - list_os[ii].pmbb(times)) < 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra(times)[ii] - list_os[ii].vra(times)) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec(times)[ii] - list_os[ii].vdec(times)) < 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll(times)[ii] - list_os[ii].vll(times)) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb(times)[ii] - list_os[ii].vbb(times)) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos(times)[ii] - list_os[ii].vlos(times)) < 1e-9
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX(times)[ii] - list_os[ii].helioX(times)) < 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY(times)[ii] - list_os[ii].helioY(times)) < 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ(times)[ii] - list_os[ii].helioZ(times)) < 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(times)[ii] - list_os[ii].U(times)) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(times)[ii] - list_os[ii].V(times)) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(times)[ii] - list_os[ii].W(times)) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord(times).ra[ii] - list_os[ii].SkyCoord(times).ra)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord(times).dec[ii] - list_os[ii].SkyCoord(times).dec)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).distance[ii] - list_os[ii].SkyCoord(times).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_ra_cosdec[ii]
- list_os[ii].SkyCoord(times).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_dec[ii] - list_os[ii].SkyCoord(times).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).radial_velocity[ii]
- list_os[ii].SkyCoord(times).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-9
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Also a single time in the array ...
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times[1]) - list_os[ii].time(times[1])) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times[1])[ii] - list_os[ii].R(times[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times[1])[ii] - list_os[ii].r(times[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times[1])[ii] - list_os[ii].vR(times[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times[1])[ii] - list_os[ii].vT(times[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times[1])[ii] - list_os[ii].z(times[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times[1])[ii] - list_os[ii].vz(times[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(times[1])[ii] - list_os[ii].phi(times[1]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x(times[1])[ii] - list_os[ii].x(times[1])) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y(times[1])[ii] - list_os[ii].y(times[1])) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times[1])[ii] - list_os[ii].vx(times[1])) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy(times[1])[ii] - list_os[ii].vy(times[1])) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times[1])[ii] - list_os[ii].vphi(times[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(times[1])[ii] - list_os[ii].ra(times[1])) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec(times[1])[ii] - list_os[ii].dec(times[1])) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist(times[1])[ii] - list_os[ii].dist(times[1])) < 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(times[1])[ii] - list_os[ii].ll(times[1])) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(times[1])[ii] - list_os[ii].bb(times[1])) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra(times[1])[ii] - list_os[ii].pmra(times[1])) < 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec(times[1])[ii] - list_os[ii].pmdec(times[1])) < 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll(times[1])[ii] - list_os[ii].pmll(times[1])) < 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb(times[1])[ii] - list_os[ii].pmbb(times[1])) < 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra(times[1])[ii] - list_os[ii].vra(times[1])) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec(times[1])[ii] - list_os[ii].vdec(times[1])) < 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll(times[1])[ii] - list_os[ii].vll(times[1])) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb(times[1])[ii] - list_os[ii].vbb(times[1])) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos(times[1])[ii] - list_os[ii].vlos(times[1])) < 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX(times[1])[ii] - list_os[ii].helioX(times[1])) < 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY(times[1])[ii] - list_os[ii].helioY(times[1])) < 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ(times[1])[ii] - list_os[ii].helioZ(times[1])) < 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(times[1])[ii] - list_os[ii].U(times[1])) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(times[1])[ii] - list_os[ii].V(times[1])) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(times[1])[ii] - list_os[ii].W(times[1])) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord(times[1]).ra[ii] - list_os[ii].SkyCoord(times[1]).ra)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times[1]).dec[ii] - list_os[ii].SkyCoord(times[1]).dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times[1]).distance[ii]
- list_os[ii].SkyCoord(times[1]).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(times[1]).pm_ra_cosdec[ii]
- list_os[ii].SkyCoord(times[1]).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times[1]).pm_dec[ii]
- list_os[ii].SkyCoord(times[1]).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times[1]).radial_velocity[ii]
- list_os[ii].SkyCoord(times[1]).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes)[ii] - list_os[ii].r(itimes)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes)[ii] - list_os[ii].vR(itimes)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes)[ii] - list_os[ii].vT(itimes)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes)[ii] - list_os[ii].z(itimes)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes)[ii] - list_os[ii].vz(itimes)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(itimes)[ii] - list_os[ii].phi(itimes) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x(itimes)[ii] - list_os[ii].x(itimes)) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y(itimes)[ii] - list_os[ii].y(itimes)) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(itimes)[ii] - list_os[ii].vx(itimes)) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy(itimes)[ii] - list_os[ii].vy(itimes)) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes)[ii] - list_os[ii].vphi(itimes)) < 1e-10
), "Evaluating Orbits vphidoes not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(itimes)[ii] - list_os[ii].ra(itimes)) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec(itimes)[ii] - list_os[ii].dec(itimes)) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist(itimes)[ii] - list_os[ii].dist(itimes)) < 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(itimes)[ii] - list_os[ii].ll(itimes)) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(itimes)[ii] - list_os[ii].bb(itimes)) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra(itimes)[ii] - list_os[ii].pmra(itimes)) < 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec(itimes)[ii] - list_os[ii].pmdec(itimes)) < 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll(itimes)[ii] - list_os[ii].pmll(itimes)) < 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb(itimes)[ii] - list_os[ii].pmbb(itimes)) < 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra(itimes)[ii] - list_os[ii].vra(itimes)) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec(itimes)[ii] - list_os[ii].vdec(itimes)) < 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll(itimes)[ii] - list_os[ii].vll(itimes)) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb(itimes)[ii] - list_os[ii].vbb(itimes)) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos(itimes)[ii] - list_os[ii].vlos(itimes)) < 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX(itimes)[ii] - list_os[ii].helioX(itimes)) < 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY(itimes)[ii] - list_os[ii].helioY(itimes)) < 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ(itimes)[ii] - list_os[ii].helioZ(itimes)) < 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(itimes)[ii] - list_os[ii].U(itimes)) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(itimes)[ii] - list_os[ii].V(itimes)) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(itimes)[ii] - list_os[ii].W(itimes)) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord(itimes).ra[ii] - list_os[ii].SkyCoord(itimes).ra)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.SkyCoord(itimes).dec[ii] - list_os[ii].SkyCoord(itimes).dec)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes).distance[ii] - list_os[ii].SkyCoord(itimes).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes).pm_ra_cosdec[ii]
- list_os[ii].SkyCoord(itimes).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes).pm_dec[ii] - list_os[ii].SkyCoord(itimes).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes).radial_velocity[ii]
- list_os[ii].SkyCoord(itimes).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes[1])[ii] - list_os[ii].r(itimes[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes[1])[ii] - list_os[ii].vR(itimes[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes[1])[ii] - list_os[ii].vT(itimes[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes[1])[ii] - list_os[ii].z(itimes[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes[1])[ii] - list_os[ii].vz(itimes[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(itimes[1])[ii] - list_os[ii].phi(itimes[1]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(itimes[1])[ii] - list_os[ii].ra(itimes[1])) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec(itimes[1])[ii] - list_os[ii].dec(itimes[1])) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist(itimes[1])[ii] - list_os[ii].dist(itimes[1])) < 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(itimes[1])[ii] - list_os[ii].ll(itimes[1])) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(itimes[1])[ii] - list_os[ii].bb(itimes[1])) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra(itimes[1])[ii] - list_os[ii].pmra(itimes[1])) < 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec(itimes[1])[ii] - list_os[ii].pmdec(itimes[1])) < 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll(itimes[1])[ii] - list_os[ii].pmll(itimes[1])) < 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb(itimes[1])[ii] - list_os[ii].pmbb(itimes[1])) < 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra(itimes[1])[ii] - list_os[ii].vra(itimes[1])) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec(itimes[1])[ii] - list_os[ii].vdec(itimes[1])) < 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll(itimes[1])[ii] - list_os[ii].vll(itimes[1])) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb(itimes[1])[ii] - list_os[ii].vbb(itimes[1])) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos(itimes[1])[ii] - list_os[ii].vlos(itimes[1])) < 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX(itimes[1])[ii] - list_os[ii].helioX(itimes[1])) < 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY(itimes[1])[ii] - list_os[ii].helioY(itimes[1])) < 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ(itimes[1])[ii] - list_os[ii].helioZ(itimes[1])) < 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(itimes[1])[ii] - list_os[ii].U(itimes[1])) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(itimes[1])[ii] - list_os[ii].V(itimes[1])) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(itimes[1])[ii] - list_os[ii].W(itimes[1])) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).ra[ii] - list_os[ii].SkyCoord(itimes[1]).ra
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).dec[ii] - list_os[ii].SkyCoord(itimes[1]).dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).distance[ii]
- list_os[ii].SkyCoord(itimes[1]).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).pm_ra_cosdec[ii]
- list_os[ii].SkyCoord(itimes[1]).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).pm_dec[ii]
- list_os[ii].SkyCoord(itimes[1]).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(itimes[1]).radial_velocity[ii]
- list_os[ii].SkyCoord(itimes[1]).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
return None
# Test that evaluating coordinate functions for integrated orbits works,
# for 5D orbits
def test_coordinate_interpolation_5d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 20
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs)))
list_os = [
Orbit([R, vR, vT, z, vz]) for R, vR, vT, z, vz in zip(Rs, vRs, vTs, zs, vzs)
]
# Before integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R()[ii] - list_os[ii].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii] - list_os[ii].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii] - list_os[ii].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii] - list_os[ii].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z()[ii] - list_os[ii].z()) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz()[ii] - list_os[ii].vz()) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii] - list_os[ii].vphi()) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(times)[ii] - list_os[ii].R(times)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii] - list_os[ii].r(times)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii] - list_os[ii].vR(times)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii] - list_os[ii].vT(times)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times)[ii] - list_os[ii].z(times)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times)[ii] - list_os[ii].vz(times)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times)[ii] - list_os[ii].vphi(times)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(times[1])[ii] - list_os[ii].R(times[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times[1])[ii] - list_os[ii].r(times[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times[1])[ii] - list_os[ii].vR(times[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times[1])[ii] - list_os[ii].vT(times[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times[1])[ii] - list_os[ii].z(times[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times[1])[ii] - list_os[ii].vz(times[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times[1])[ii] - list_os[ii].vphi(times[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes)[ii] - list_os[ii].r(itimes)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes)[ii] - list_os[ii].vR(itimes)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes)[ii] - list_os[ii].vT(itimes)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes)[ii] - list_os[ii].z(itimes)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes)[ii] - list_os[ii].vz(itimes)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes)[ii] - list_os[ii].vphi(itimes)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes[1])[ii] - list_os[ii].r(itimes[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes[1])[ii] - list_os[ii].vR(itimes[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes[1])[ii] - list_os[ii].vT(itimes[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes[1])[ii] - list_os[ii].z(itimes[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes[1])[ii] - list_os[ii].vz(itimes[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes[1])[ii] - list_os[ii].vphi(itimes[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
with pytest.raises(AttributeError):
os.phi()
return None
# Test that evaluating coordinate functions for integrated orbits works,
# for 4D orbits
def test_coordinate_interpolation_4d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 20
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, phis)))
list_os = [Orbit([R, vR, vT, phi]) for R, vR, vT, phi in zip(Rs, vRs, vTs, phis)]
# Before integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R()[ii] - list_os[ii].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii] - list_os[ii].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii] - list_os[ii].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii] - list_os[ii].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.phi()[ii] - list_os[ii].phi()) < 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii] - list_os[ii].vphi()) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(times)[ii] - list_os[ii].R(times)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii] - list_os[ii].r(times)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii] - list_os[ii].vR(times)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii] - list_os[ii].vT(times)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.phi(times)[ii] - list_os[ii].phi(times)) < 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times)[ii] - list_os[ii].vphi(times)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(times[1])[ii] - list_os[ii].R(times[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times[1])[ii] - list_os[ii].r(times[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times[1])[ii] - list_os[ii].vR(times[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times[1])[ii] - list_os[ii].vT(times[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.phi(times[1])[ii] - list_os[ii].phi(times[1])) < 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times[1])[ii] - list_os[ii].vphi(times[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes)[ii] - list_os[ii].r(itimes)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes)[ii] - list_os[ii].vR(itimes)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes)[ii] - list_os[ii].vT(itimes)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.phi(itimes)[ii] - list_os[ii].phi(itimes)) < 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes)[ii] - list_os[ii].vphi(itimes)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes[1])[ii] - list_os[ii].r(itimes[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes[1])[ii] - list_os[ii].vR(itimes[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes[1])[ii] - list_os[ii].vT(itimes[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.phi(itimes[1])[ii] - list_os[ii].phi(itimes[1])) < 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes[1])[ii] - list_os[ii].vphi(itimes[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
with pytest.raises(AttributeError):
os.z()
with pytest.raises(AttributeError):
os.vz()
return None
# Test that evaluating coordinate functions for integrated orbits works,
# for 3D orbits
def test_coordinate_interpolation_3d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 20
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
os = Orbit(list(zip(Rs, vRs, vTs)))
list_os = [Orbit([R, vR, vT]) for R, vR, vT in zip(Rs, vRs, vTs)]
# Before integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R()[ii] - list_os[ii].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii] - list_os[ii].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii] - list_os[ii].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii] - list_os[ii].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii] - list_os[ii].vphi()) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(times)[ii] - list_os[ii].R(times)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii] - list_os[ii].r(times)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii] - list_os[ii].vR(times)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii] - list_os[ii].vT(times)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times)[ii] - list_os[ii].vphi(times)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(times[1])[ii] - list_os[ii].R(times[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times[1])[ii] - list_os[ii].r(times[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times[1])[ii] - list_os[ii].vR(times[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times[1])[ii] - list_os[ii].vT(times[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(times[1])[ii] - list_os[ii].vphi(times[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes)[ii] - list_os[ii].r(itimes)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes)[ii] - list_os[ii].vR(itimes)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes)[ii] - list_os[ii].vT(itimes)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes)[ii] - list_os[ii].vphi(itimes)) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes[1])[ii] - list_os[ii].r(itimes[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes[1])[ii] - list_os[ii].vR(itimes[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes[1])[ii] - list_os[ii].vT(itimes[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi(itimes[1])[ii] - list_os[ii].vphi(itimes[1])) < 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
with pytest.raises(AttributeError):
os.phi()
with pytest.raises(AttributeError):
os.x()
with pytest.raises(AttributeError):
os.vx()
with pytest.raises(AttributeError):
os.y()
with pytest.raises(AttributeError):
os.vy()
return None
# Test that evaluating coordinate functions for integrated orbits works,
# for 2D orbits
def test_coordinate_interpolation_2d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014, toVerticalPotential
MWPotential2014 = toVerticalPotential(MWPotential2014, 1.0)
numpy.random.seed(1)
nrand = 20
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(zs, vzs)))
list_os = [Orbit([z, vz]) for z, vz in zip(zs, vzs)]
# Before integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.x()[ii] - list_os[ii].x()) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx()[ii] - list_os[ii].vx()) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.x(times)[ii] - list_os[ii].x(times)) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times)[ii] - list_os[ii].vx(times)) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.x(times[1])[ii] - list_os[ii].x(times[1])) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times[1])[ii] - list_os[ii].vx(times[1])) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.x(itimes)[ii] - list_os[ii].x(itimes)) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(itimes)[ii] - list_os[ii].vx(itimes)) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.x(itimes[1])[ii] - list_os[ii].x(itimes[1])) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(itimes[1])[ii] - list_os[ii].vx(itimes[1])) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
return None
# Test interpolation with backwards orbit integration
def test_backinterpolation():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 20
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# Integrate all
times = numpy.linspace(0.0, -10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
return None
# Test that evaluating coordinate functions for integrated orbits works for
# a single orbit
def test_coordinate_interpolation_oneorbit():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 1
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# Before integration
for ii in range(nrand):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time() - list_os[ii].time()) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R()[ii] - list_os[ii].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii] - list_os[ii].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii] - list_os[ii].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii] - list_os[ii].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z()[ii] - list_os[ii].z()) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz()[ii] - list_os[ii].vz()) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
((os.phi()[ii] - list_os[ii].phi() + numpy.pi) % (2.0 * numpy.pi))
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
# Test exact times of integration
for ii in range(nrand):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times) - list_os[ii].time(times)) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times)[ii] - list_os[ii].R(times)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii] - list_os[ii].r(times)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii] - list_os[ii].vR(times)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii] - list_os[ii].vT(times)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times)[ii] - list_os[ii].z(times)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times)[ii] - list_os[ii].vz(times)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(times)[ii] - list_os[ii].phi(times) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
# Also a single time in the array ...
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times[1]) - list_os[ii].time(times[1])) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times[1])[ii] - list_os[ii].R(times[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times[1])[ii] - list_os[ii].r(times[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times[1])[ii] - list_os[ii].vR(times[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times[1])[ii] - list_os[ii].vT(times[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times[1])[ii] - list_os[ii].z(times[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times[1])[ii] - list_os[ii].vz(times[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(times[1])[ii] - list_os[ii].phi(times[1]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
# Test actual interpolated
itimes = times[:-2] + (times[1] - times[0]) / 2.0
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.R(itimes)[ii] - list_os[ii].R(itimes)) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes)[ii] - list_os[ii].r(itimes)) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes)[ii] - list_os[ii].vR(itimes)) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes)[ii] - list_os[ii].vT(itimes)) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes)[ii] - list_os[ii].z(itimes)) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes)[ii] - list_os[ii].vz(itimes)) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(itimes)[ii] - list_os[ii].phi(itimes) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
# Also a single time in the array ...
assert numpy.all(
numpy.fabs(os.R(itimes[1])[ii] - list_os[ii].R(itimes[1])) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(itimes[1])[ii] - list_os[ii].r(itimes[1])) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(itimes[1])[ii] - list_os[ii].vR(itimes[1])) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(itimes[1])[ii] - list_os[ii].vT(itimes[1])) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(itimes[1])[ii] - list_os[ii].z(itimes[1])) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(itimes[1])[ii] - list_os[ii].vz(itimes[1])) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(os.phi(itimes[1])[ii] - list_os[ii].phi(itimes[1]) + numpy.pi)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
return None
# Test that an error is raised when evaluating an orbit outside of the
# integration range
def test_interpolate_outsiderange():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 3
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
with pytest.raises(ValueError) as excinfo:
os.R(11.0)
with pytest.raises(ValueError) as excinfo:
os.R(-1.0)
# Also for arrays that partially overlap
with pytest.raises(ValueError) as excinfo:
os.R(numpy.linspace(5.0, 11.0, 1001))
with pytest.raises(ValueError) as excinfo:
os.R(numpy.linspace(-5.0, 5.0, 1001))
def test_output_shape():
# Test that the output shape is correct and that the shaped output is correct
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = (3, 1, 2)
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vxvv = numpy.rollaxis(numpy.array([Rs, vRs, vTs, zs, vzs, phis]), 0, 4)
os = Orbit(vxvv)
list_os = [
[
[
Orbit(
[
Rs[ii, jj, kk],
vRs[ii, jj, kk],
vTs[ii, jj, kk],
zs[ii, jj, kk],
vzs[ii, jj, kk],
phis[ii, jj, kk],
]
)
for kk in range(nrand[2])
]
for jj in range(nrand[1])
]
for ii in range(nrand[0])
]
# Before integration
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time() - list_os[ii][jj][kk].time()) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R()[ii, jj, kk] - list_os[ii][jj][kk].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii, jj, kk] - list_os[ii][jj][kk].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii, jj, kk] - list_os[ii][jj][kk].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii, jj, kk] - list_os[ii][jj][kk].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z()[ii, jj, kk] - list_os[ii][jj][kk].z()) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz()[ii, jj, kk] - list_os[ii][jj][kk].vz()) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(
os.phi()[ii, jj, kk]
- list_os[ii][jj][kk].phi()
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x()[ii, jj, kk] - list_os[ii][jj][kk].x()) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y()[ii, jj, kk] - list_os[ii][jj][kk].y()) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx()[ii, jj, kk] - list_os[ii][jj][kk].vx()) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy()[ii, jj, kk] - list_os[ii][jj][kk].vy()) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii, jj, kk] - list_os[ii][jj][kk].vphi())
< 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra()[ii, jj, kk] - list_os[ii][jj][kk].ra()) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec()[ii, jj, kk] - list_os[ii][jj][kk].dec()) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist()[ii, jj, kk] - list_os[ii][jj][kk].dist())
< 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll()[ii, jj, kk] - list_os[ii][jj][kk].ll()) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb()[ii, jj, kk] - list_os[ii][jj][kk].bb()) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra()[ii, jj, kk] - list_os[ii][jj][kk].pmra())
< 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec()[ii, jj, kk] - list_os[ii][jj][kk].pmdec())
< 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll()[ii, jj, kk] - list_os[ii][jj][kk].pmll())
< 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb()[ii, jj, kk] - list_os[ii][jj][kk].pmbb())
< 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra()[ii, jj, kk] - list_os[ii][jj][kk].vra()) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec()[ii, jj, kk] - list_os[ii][jj][kk].vdec())
< 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll()[ii, jj, kk] - list_os[ii][jj][kk].vll()) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb()[ii, jj, kk] - list_os[ii][jj][kk].vbb()) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos()[ii, jj, kk] - list_os[ii][jj][kk].vlos())
< 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX()[ii, jj, kk] - list_os[ii][jj][kk].helioX())
< 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY()[ii, jj, kk] - list_os[ii][jj][kk].helioY())
< 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ()[ii, jj, kk] - list_os[ii][jj][kk].helioZ())
< 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U()[ii, jj, kk] - list_os[ii][jj][kk].U()) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V()[ii, jj, kk] - list_os[ii][jj][kk].V()) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W()[ii, jj, kk] - list_os[ii][jj][kk].W()) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().ra[ii, jj, kk] - list_os[ii][jj][kk].SkyCoord().ra
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().distance[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord().pm_ra_cosdec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().pm_dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().radial_velocity[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
list_os[ii][jj][kk].integrate(times, MWPotential2014)
# Test exact times of integration
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times) - list_os[ii][jj][kk].time(times)) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times)[ii, jj, kk] - list_os[ii][jj][kk].R(times))
< 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii, jj, kk] - list_os[ii][jj][kk].r(times))
< 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii, jj, kk] - list_os[ii][jj][kk].vR(times))
< 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii, jj, kk] - list_os[ii][jj][kk].vT(times))
< 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times)[ii, jj, kk] - list_os[ii][jj][kk].z(times))
< 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times)[ii, jj, kk] - list_os[ii][jj][kk].vz(times))
< 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(
os.phi(times)[ii, jj, kk]
- list_os[ii][jj][kk].phi(times)
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x(times)[ii, jj, kk] - list_os[ii][jj][kk].x(times))
< 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y(times)[ii, jj, kk] - list_os[ii][jj][kk].y(times))
< 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times)[ii, jj, kk] - list_os[ii][jj][kk].vx(times))
< 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy(times)[ii, jj, kk] - list_os[ii][jj][kk].vy(times))
< 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vphi(times)[ii, jj, kk] - list_os[ii][jj][kk].vphi(times)
)
< 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(times)[ii, jj, kk] - list_os[ii][jj][kk].ra(times))
< 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.dec(times)[ii, jj, kk] - list_os[ii][jj][kk].dec(times)
)
< 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.dist(times)[ii, jj, kk] - list_os[ii][jj][kk].dist(times)
)
< 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(times)[ii, jj, kk] - list_os[ii][jj][kk].ll(times))
< 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(times)[ii, jj, kk] - list_os[ii][jj][kk].bb(times))
< 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmra(times)[ii, jj, kk] - list_os[ii][jj][kk].pmra(times)
)
< 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmdec(times)[ii, jj, kk] - list_os[ii][jj][kk].pmdec(times)
)
< 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmll(times)[ii, jj, kk] - list_os[ii][jj][kk].pmll(times)
)
< 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmbb(times)[ii, jj, kk] - list_os[ii][jj][kk].pmbb(times)
)
< 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vra(times)[ii, jj, kk] - list_os[ii][jj][kk].vra(times)
)
< 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vdec(times)[ii, jj, kk] - list_os[ii][jj][kk].vdec(times)
)
< 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vll(times)[ii, jj, kk] - list_os[ii][jj][kk].vll(times)
)
< 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vbb(times)[ii, jj, kk] - list_os[ii][jj][kk].vbb(times)
)
< 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vlos(times)[ii, jj, kk] - list_os[ii][jj][kk].vlos(times)
)
< 1e-9
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioX(times)[ii, jj, kk] - list_os[ii][jj][kk].helioX(times)
)
< 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioY(times)[ii, jj, kk] - list_os[ii][jj][kk].helioY(times)
)
< 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioZ(times)[ii, jj, kk] - list_os[ii][jj][kk].helioZ(times)
)
< 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(times)[ii, jj, kk] - list_os[ii][jj][kk].U(times))
< 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(times)[ii, jj, kk] - list_os[ii][jj][kk].V(times))
< 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(times)[ii, jj, kk] - list_os[ii][jj][kk].W(times))
< 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).ra[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).ra
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).distance[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_ra_cosdec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).radial_velocity[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-9
), "Evaluating Orbits SkyCoord does not agree with Orbit"
return None
def test_output_reshape():
# Test that the output shape is correct and that the shaped output is correct
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = (3, 1, 2)
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vxvv = numpy.rollaxis(numpy.array([Rs, vRs, vTs, zs, vzs, phis]), 0, 4)
os = Orbit(vxvv)
# NOW RESHAPE
# First try a shape that doesn't work to test the error
with pytest.raises(ValueError) as excinfo:
os.reshape((4, 2, 1))
# then do one that should work and also setup the list of indiv orbits
# with the new shape
newshape = (3, 2, 1)
os.reshape(newshape)
Rs = Rs.reshape(newshape)
vRs = vRs.reshape(newshape)
vTs = vTs.reshape(newshape)
zs = zs.reshape(newshape)
vzs = vzs.reshape(newshape)
phis = phis.reshape(newshape)
nrand = newshape
list_os = [
[
[
Orbit(
[
Rs[ii, jj, kk],
vRs[ii, jj, kk],
vTs[ii, jj, kk],
zs[ii, jj, kk],
vzs[ii, jj, kk],
phis[ii, jj, kk],
]
)
for kk in range(nrand[2])
]
for jj in range(nrand[1])
]
for ii in range(nrand[0])
]
# Before integration
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time() - list_os[ii][jj][kk].time()) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R()[ii, jj, kk] - list_os[ii][jj][kk].R()) < 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r()[ii, jj, kk] - list_os[ii][jj][kk].r()) < 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR()[ii, jj, kk] - list_os[ii][jj][kk].vR()) < 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT()[ii, jj, kk] - list_os[ii][jj][kk].vT()) < 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z()[ii, jj, kk] - list_os[ii][jj][kk].z()) < 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz()[ii, jj, kk] - list_os[ii][jj][kk].vz()) < 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(
os.phi()[ii, jj, kk]
- list_os[ii][jj][kk].phi()
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x()[ii, jj, kk] - list_os[ii][jj][kk].x()) < 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y()[ii, jj, kk] - list_os[ii][jj][kk].y()) < 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx()[ii, jj, kk] - list_os[ii][jj][kk].vx()) < 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy()[ii, jj, kk] - list_os[ii][jj][kk].vy()) < 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vphi()[ii, jj, kk] - list_os[ii][jj][kk].vphi())
< 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra()[ii, jj, kk] - list_os[ii][jj][kk].ra()) < 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dec()[ii, jj, kk] - list_os[ii][jj][kk].dec()) < 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.dist()[ii, jj, kk] - list_os[ii][jj][kk].dist())
< 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll()[ii, jj, kk] - list_os[ii][jj][kk].ll()) < 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb()[ii, jj, kk] - list_os[ii][jj][kk].bb()) < 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmra()[ii, jj, kk] - list_os[ii][jj][kk].pmra())
< 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmdec()[ii, jj, kk] - list_os[ii][jj][kk].pmdec())
< 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmll()[ii, jj, kk] - list_os[ii][jj][kk].pmll())
< 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.pmbb()[ii, jj, kk] - list_os[ii][jj][kk].pmbb())
< 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vra()[ii, jj, kk] - list_os[ii][jj][kk].vra()) < 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vdec()[ii, jj, kk] - list_os[ii][jj][kk].vdec())
< 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vll()[ii, jj, kk] - list_os[ii][jj][kk].vll()) < 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vbb()[ii, jj, kk] - list_os[ii][jj][kk].vbb()) < 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vlos()[ii, jj, kk] - list_os[ii][jj][kk].vlos())
< 1e-10
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioX()[ii, jj, kk] - list_os[ii][jj][kk].helioX())
< 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioY()[ii, jj, kk] - list_os[ii][jj][kk].helioY())
< 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.helioZ()[ii, jj, kk] - list_os[ii][jj][kk].helioZ())
< 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U()[ii, jj, kk] - list_os[ii][jj][kk].U()) < 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V()[ii, jj, kk] - list_os[ii][jj][kk].V()) < 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W()[ii, jj, kk] - list_os[ii][jj][kk].W()) < 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().ra[ii, jj, kk] - list_os[ii][jj][kk].SkyCoord().ra
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().distance[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord().pm_ra_cosdec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().pm_dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord().radial_velocity[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord().radial_velocity
)
.to(u.km / u.s)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
list_os[ii][jj][kk].integrate(times, MWPotential2014)
# Test exact times of integration
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
# .time is special, just a single array
assert numpy.all(
numpy.fabs(os.time(times) - list_os[ii][jj][kk].time(times)) < 1e-10
), "Evaluating Orbits time does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.R(times)[ii, jj, kk] - list_os[ii][jj][kk].R(times))
< 1e-10
), "Evaluating Orbits R does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.r(times)[ii, jj, kk] - list_os[ii][jj][kk].r(times))
< 1e-10
), "Evaluating Orbits r does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vR(times)[ii, jj, kk] - list_os[ii][jj][kk].vR(times))
< 1e-10
), "Evaluating Orbits vR does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vT(times)[ii, jj, kk] - list_os[ii][jj][kk].vT(times))
< 1e-10
), "Evaluating Orbits vT does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.z(times)[ii, jj, kk] - list_os[ii][jj][kk].z(times))
< 1e-10
), "Evaluating Orbits z does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vz(times)[ii, jj, kk] - list_os[ii][jj][kk].vz(times))
< 1e-10
), "Evaluating Orbits vz does not agree with Orbit"
assert numpy.all(
numpy.fabs(
(
(
os.phi(times)[ii, jj, kk]
- list_os[ii][jj][kk].phi(times)
+ numpy.pi
)
% (2.0 * numpy.pi)
)
- numpy.pi
)
< 1e-10
), "Evaluating Orbits phi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.x(times)[ii, jj, kk] - list_os[ii][jj][kk].x(times))
< 1e-10
), "Evaluating Orbits x does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.y(times)[ii, jj, kk] - list_os[ii][jj][kk].y(times))
< 1e-10
), "Evaluating Orbits y does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vx(times)[ii, jj, kk] - list_os[ii][jj][kk].vx(times))
< 1e-10
), "Evaluating Orbits vx does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.vy(times)[ii, jj, kk] - list_os[ii][jj][kk].vy(times))
< 1e-10
), "Evaluating Orbits vy does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vphi(times)[ii, jj, kk] - list_os[ii][jj][kk].vphi(times)
)
< 1e-10
), "Evaluating Orbits vphi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ra(times)[ii, jj, kk] - list_os[ii][jj][kk].ra(times))
< 1e-10
), "Evaluating Orbits ra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.dec(times)[ii, jj, kk] - list_os[ii][jj][kk].dec(times)
)
< 1e-10
), "Evaluating Orbits dec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.dist(times)[ii, jj, kk] - list_os[ii][jj][kk].dist(times)
)
< 1e-10
), "Evaluating Orbits dist does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.ll(times)[ii, jj, kk] - list_os[ii][jj][kk].ll(times))
< 1e-10
), "Evaluating Orbits ll does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.bb(times)[ii, jj, kk] - list_os[ii][jj][kk].bb(times))
< 1e-10
), "Evaluating Orbits bb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmra(times)[ii, jj, kk] - list_os[ii][jj][kk].pmra(times)
)
< 1e-10
), "Evaluating Orbits pmra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmdec(times)[ii, jj, kk] - list_os[ii][jj][kk].pmdec(times)
)
< 1e-10
), "Evaluating Orbits pmdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmll(times)[ii, jj, kk] - list_os[ii][jj][kk].pmll(times)
)
< 1e-10
), "Evaluating Orbits pmll does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.pmbb(times)[ii, jj, kk] - list_os[ii][jj][kk].pmbb(times)
)
< 1e-10
), "Evaluating Orbits pmbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vra(times)[ii, jj, kk] - list_os[ii][jj][kk].vra(times)
)
< 1e-10
), "Evaluating Orbits vra does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vdec(times)[ii, jj, kk] - list_os[ii][jj][kk].vdec(times)
)
< 1e-10
), "Evaluating Orbits vdec does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vll(times)[ii, jj, kk] - list_os[ii][jj][kk].vll(times)
)
< 1e-10
), "Evaluating Orbits vll does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vbb(times)[ii, jj, kk] - list_os[ii][jj][kk].vbb(times)
)
< 1e-10
), "Evaluating Orbits vbb does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.vlos(times)[ii, jj, kk] - list_os[ii][jj][kk].vlos(times)
)
< 1e-9
), "Evaluating Orbits vlos does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioX(times)[ii, jj, kk] - list_os[ii][jj][kk].helioX(times)
)
< 1e-10
), "Evaluating Orbits helioX does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioY(times)[ii, jj, kk] - list_os[ii][jj][kk].helioY(times)
)
< 1e-10
), "Evaluating Orbits helioY does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.helioZ(times)[ii, jj, kk] - list_os[ii][jj][kk].helioZ(times)
)
< 1e-10
), "Evaluating Orbits helioZ does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.U(times)[ii, jj, kk] - list_os[ii][jj][kk].U(times))
< 1e-10
), "Evaluating Orbits U does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.V(times)[ii, jj, kk] - list_os[ii][jj][kk].V(times))
< 1e-10
), "Evaluating Orbits V does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.W(times)[ii, jj, kk] - list_os[ii][jj][kk].W(times))
< 1e-10
), "Evaluating Orbits W does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).ra[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).ra
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).dec
)
.to(u.deg)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).distance[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).distance
)
.to(u.kpc)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
if _APY3:
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_ra_cosdec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).pm_ra_cosdec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).pm_dec[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).pm_dec
)
.to(u.mas / u.yr)
.value
< 1e-10
), "Evaluating Orbits SkyCoord does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.SkyCoord(times).radial_velocity[ii, jj, kk]
- list_os[ii][jj][kk].SkyCoord(times).radial_velocity
)
.to(u.km / u.s)
.value
< 1e-9
), "Evaluating Orbits SkyCoord does not agree with Orbit"
return None
def test_output_specialshapes():
# Test that the output shape is correct and that the shaped output is correct, for 'special' inputs (single objects, ...)
from galpy.orbit import Orbit
# vxvv= list of [R,vR,vT,z,...] should be shape == () and scalar output
os = Orbit([1.0, 0.1, 1.0, 0.1, 0.0, 0.1])
assert os.shape == (), "Shape of Orbits with list of [R,vR,...] input is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with list of [R,vR,...] input does not return scalar"
# Similar for list [ra,dec,...]
os = Orbit([1.0, 0.1, 1.0, 0.1, 0.0, 0.1], radec=True)
assert (
os.shape == ()
), "Shape of Orbits with list of [ra,dec,...] input is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with list of [ra,dec,...] input does not return scalar"
# Also with units
os = Orbit(
[
1.0 * u.deg,
0.1 * u.rad,
1.0 * u.pc,
0.1 * u.mas / u.yr,
0.0 * u.arcsec / u.yr,
0.1 * u.pc / u.Myr,
],
radec=True,
)
assert (
os.shape == ()
), "Shape of Orbits with list of [ra,dec,...] w/units input is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with list of [ra,dec,...] w/units input does not return scalar"
# Also from_name
os = Orbit.from_name("LMC")
assert os.shape == (), "Shape of Orbits with from_name single object is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with from_name single object does not return scalar"
# vxvv= list of list of [R,vR,vT,z,...] should be shape == (1,) and array output
os = Orbit([[1.0, 0.1, 1.0, 0.1, 0.0, 0.1]])
assert os.shape == (
1,
), "Shape of Orbits with list of list of [R,vR,...] input is not (1,)"
assert (
numpy.ndim(os.R()) == 1
), "Orbits with list of list of [R,vR,...] input does not return array"
# vxvv= array of [R,vR,vT,z,...] should be shape == () and scalar output
os = Orbit(numpy.array([1.0, 0.1, 1.0, 0.1, 0.0, 0.1]))
assert os.shape == (), "Shape of Orbits with array of [R,vR,...] input is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with array of [R,vR,...] input does not return scalar"
if _APY3:
# vxvv= single SkyCoord should be shape == () and scalar output
co = apycoords.SkyCoord(
ra=1.0 * u.deg,
dec=0.5 * u.rad,
distance=2.0 * u.kpc,
pm_ra_cosdec=-0.1 * u.mas / u.yr,
pm_dec=10.0 * u.mas / u.yr,
radial_velocity=10.0 * u.km / u.s,
frame="icrs",
)
os = Orbit(co)
assert (
os.shape == co.shape
), "Shape of Orbits with SkyCoord does not agree with shape of SkyCoord"
# vxvv= single SkyCoord, but as array should be shape == (1,) and array output
s = numpy.ones(1)
co = apycoords.SkyCoord(
ra=s * 1.0 * u.deg,
dec=s * 0.5 * u.rad,
distance=s * 2.0 * u.kpc,
pm_ra_cosdec=-0.1 * u.mas / u.yr * s,
pm_dec=10.0 * u.mas / u.yr * s,
radial_velocity=10.0 * u.km / u.s * s,
frame="icrs",
)
os = Orbit(co)
assert (
os.shape == co.shape
), "Shape of Orbits with SkyCoord does not agree with shape of SkyCoord"
# vxvv= None should be shape == (1,) and array output
os = Orbit()
assert os.shape == (), "Shape of Orbits with vxvv=None input is not empty"
assert (
numpy.ndim(os.R()) == 0
), "Orbits with with vxvv=None input does not return scalar"
return None
def test_call_issue256():
# Same as for Orbit instances: non-integrated orbit with t=/=0 should return error
from galpy.orbit import Orbit
o = Orbit(vxvv=[[5.0, -1.0, 0.8, 3, -0.1, 0]])
# no integration of the orbit
with pytest.raises(ValueError) as excinfo:
o.R(30)
return None
# Test that the energy, angular momentum, and Jacobi functions work as expected
def test_energy_jacobi_angmom():
from galpy.orbit import Orbit
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
# 6D
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
_check_energy_jacobi_angmom(os, list_os)
# 5D
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs)))
list_os = [
Orbit([R, vR, vT, z, vz]) for R, vR, vT, z, vz in zip(Rs, vRs, vTs, zs, vzs)
]
_check_energy_jacobi_angmom(os, list_os)
# 4D
os = Orbit(list(zip(Rs, vRs, vTs, phis)))
list_os = [Orbit([R, vR, vT, phi]) for R, vR, vT, phi in zip(Rs, vRs, vTs, phis)]
_check_energy_jacobi_angmom(os, list_os)
# 3D
os = Orbit(list(zip(Rs, vRs, vTs)))
list_os = [Orbit([R, vR, vT]) for R, vR, vT in zip(Rs, vRs, vTs)]
_check_energy_jacobi_angmom(os, list_os)
# 2D
os = Orbit(list(zip(zs, vzs)))
list_os = [Orbit([z, vz]) for z, vz in zip(zs, vzs)]
_check_energy_jacobi_angmom(os, list_os)
return None
def _check_energy_jacobi_angmom(os, list_os):
nrand = len(os)
from galpy.potential import (
DehnenBarPotential,
DoubleExponentialDiskPotential,
MWPotential2014,
SpiralArmsPotential,
)
sp = SpiralArmsPotential()
dp = DehnenBarPotential()
lp = DoubleExponentialDiskPotential(normalize=1.0)
if os.dim() == 1:
from galpy.potential import toVerticalPotential
MWPotential2014 = toVerticalPotential(MWPotential2014, 1.0)
lp = toVerticalPotential(lp, 1.0)
# Before integration
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.E(pot=MWPotential2014)[ii] / list_os[ii].E(pot=MWPotential2014) - 1.0
)
< 10.0**-10.0
), "Evaluating Orbits E does not agree with Orbit"
if os.dim() == 3:
assert numpy.all(
numpy.fabs(
os.ER(pot=MWPotential2014)[ii] / list_os[ii].ER(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits ER does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.Ez(pot=MWPotential2014)[ii] / list_os[ii].Ez(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Ez does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.L()[ii] / list_os[ii].L() - 1.0) < 10.0**-10.0
), "Evaluating Orbits L does not agree with Orbit"
if os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Lz()[ii] / list_os[ii].Lz() - 1.0) < 10.0**-10.0
), "Evaluating Orbits Lz does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=MWPotential2014)[ii]
/ list_os[ii].Jacobi(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Also explicitly set OmegaP
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=MWPotential2014, OmegaP=0.6)[ii]
/ list_os[ii].Jacobi(pot=MWPotential2014, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Potential for which array evaluation definitely does not work
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.E(pot=lp)[ii] / list_os[ii].E(pot=lp) - 1.0) < 10.0**-10.0
), "Evaluating Orbits E does not agree with Orbit"
if os.dim() == 3:
assert numpy.all(
numpy.fabs(os.ER(pot=lp)[ii] / list_os[ii].ER(pot=lp) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits ER does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.Ez(pot=lp)[ii] / list_os[ii].Ez(pot=lp) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits Ez does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.L()[ii] / list_os[ii].L() - 1.0) < 10.0**-10.0
), "Evaluating Orbits L does not agree with Orbit"
if os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Lz()[ii] / list_os[ii].Lz() - 1.0) < 10.0**-10.0
), "Evaluating Orbits Lz does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Jacobi(pot=lp)[ii] / list_os[ii].Jacobi(pot=lp) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Also explicitly set OmegaP
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=lp, OmegaP=0.6)[ii]
/ list_os[ii].Jacobi(pot=lp, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
if os.phasedim() == 6:
# Also in 3D
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=lp, OmegaP=[0.0, 0.0, 0.6])[ii]
/ list_os[ii].Jacobi(pot=lp, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=lp, OmegaP=numpy.array([0.0, 0.0, 0.6]))[ii]
/ list_os[ii].Jacobi(pot=lp, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# o.E before integration gives AttributeError
with pytest.raises(AttributeError):
os.E()
with pytest.raises(AttributeError):
os.Jacobi()
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
for ii in range(nrand):
# Don't have to specify the potential or set to None
assert numpy.all(
numpy.fabs(
os.E(times)[ii] / list_os[ii].E(times, pot=MWPotential2014) - 1.0
)
< 10.0**-10.0
), "Evaluating Orbits E does not agree with Orbit"
if os.dim() == 3:
assert numpy.all(
numpy.fabs(
os.ER(times, pot=None)[ii]
/ list_os[ii].ER(times, pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits ER does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.Ez(times)[ii] / list_os[ii].Ez(times, pot=MWPotential2014) - 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Ez does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.L(times)[ii] / list_os[ii].L(times) - 1.0) < 10.0**-10.0
), "Evaluating Orbits L does not agree with Orbit"
if os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Lz(times)[ii] / list_os[ii].Lz(times) - 1.0) < 10.0**-10.0
), "Evaluating Orbits Lz does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Jacobi(times)[ii] / list_os[ii].Jacobi(times) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Also explicitly set OmegaP
assert numpy.all(
numpy.fabs(
os.Jacobi(times, pot=MWPotential2014, OmegaP=0.6)[ii]
/ list_os[ii].Jacobi(times, pot=MWPotential2014, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
if os.phasedim() == 6:
# Also in 3D
assert numpy.all(
numpy.fabs(
os.Jacobi(times, pot=MWPotential2014, OmegaP=[0.0, 0.0, 0.6])[ii]
/ list_os[ii].Jacobi(times, pot=MWPotential2014, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.Jacobi(
times, pot=MWPotential2014, OmegaP=numpy.array([0.0, 0.0, 0.6])
)[ii]
/ list_os[ii].Jacobi(times, pot=MWPotential2014, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Don't do non-axi for odd-D Orbits or 1D
if os.phasedim() % 2 == 1 or os.dim() == 1:
return None
# Add bar and spiral
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.E(pot=MWPotential2014 + dp + sp)[ii]
/ list_os[ii].E(pot=MWPotential2014 + dp + sp)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits E does not agree with Orbit"
if os.dim() == 3:
assert numpy.all(
numpy.fabs(
os.ER(pot=MWPotential2014 + dp + sp)[ii]
/ list_os[ii].ER(pot=MWPotential2014 + dp + sp)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits ER does not agree with Orbit"
assert numpy.all(
numpy.fabs(
os.Ez(pot=MWPotential2014 + dp + sp)[ii]
/ list_os[ii].Ez(pot=MWPotential2014 + dp + sp)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Ez does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(os.L()[ii] / list_os[ii].L() - 1.0) < 10.0**-10.0
), "Evaluating Orbits L does not agree with Orbit"
if os.dim() != 1:
assert numpy.all(
numpy.fabs(os.Lz()[ii] / list_os[ii].Lz() - 1.0) < 10.0**-10.0
), "Evaluating Orbits Lz does not agree with Orbit"
if os.phasedim() % 2 == 0 and os.dim() != 1:
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=MWPotential2014 + dp + sp)[ii]
/ list_os[ii].Jacobi(pot=MWPotential2014 + dp + sp)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
# Also explicitly set OmegaP
assert numpy.all(
numpy.fabs(
os.Jacobi(pot=MWPotential2014 + dp + sp, OmegaP=0.6)[ii]
/ list_os[ii].Jacobi(pot=MWPotential2014 + dp + sp, OmegaP=0.6)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits Jacobi does not agree with Orbit"
return None
# Test that L cannot be computed for (a) linearOrbits and (b) 5D orbits
def test_angmom_errors():
from galpy.orbit import Orbit
o = Orbit([[1.0, 0.1]])
with pytest.raises(AttributeError):
o.L()
o = Orbit([[1.0, 0.1, 1.1, 0.1, -0.2]])
with pytest.raises(AttributeError):
o.L()
return None
# Test that we can still get outputs when there aren't enough points for an actual interpolation
# Test whether Orbits evaluation methods sound warning when called with
# unitless time when orbit is integrated with unitfull times
def test_orbits_method_integrate_t_asQuantity_warning():
from astropy import units
from test_orbit import check_integrate_t_asQuantity_warning
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
# Setup and integrate orbit
ts = numpy.linspace(0.0, 10.0, 1001) * units.Gyr
o = Orbit([[1.1, 0.1, 1.1, 0.1, 0.1, 0.2], [1.1, 0.1, 1.1, 0.1, 0.1, 0.2]])
o.integrate(ts, MWPotential2014)
# Now check
check_integrate_t_asQuantity_warning(o, "R")
return None
# Test new orbits formed from __call__
def test_newOrbits():
from galpy.orbit import Orbit
o = Orbit([[1.0, 0.1, 1.1, 0.1, 0.0, 0.0], [1.1, 0.3, 0.9, -0.2, 0.3, 2.0]])
ts = numpy.linspace(0.0, 1.0, 21) # v. quick orbit integration
lp = potential.LogarithmicHaloPotential(normalize=1.0)
o.integrate(ts, lp)
no = o(ts[-1]) # new Orbits
assert numpy.all(
no.R() == o.R(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct R"
assert numpy.all(
no.vR() == o.vR(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct vR"
assert numpy.all(
no.vT() == o.vT(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct vT"
assert numpy.all(
no.z() == o.z(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct z"
assert numpy.all(
no.vz() == o.vz(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct vz"
assert numpy.all(
no.phi() == o.phi(ts[-1])
), "New Orbits formed from calling an old orbit does not have the correct phi"
assert (
not no._roSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
assert (
not no._voSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
# Also test this for multiple time outputs
nos = o(ts[-2:]) # new Orbits
assert numpy.all(
numpy.fabs(nos.R() - o.R(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct R"
assert numpy.all(
numpy.fabs(nos.vR() - o.vR(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vR"
assert numpy.all(
numpy.fabs(nos.vT() - o.vT(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vT"
assert numpy.all(
numpy.fabs(nos.z() - o.z(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct z"
assert numpy.all(
numpy.fabs(nos.vz() - o.vz(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vz"
assert numpy.all(
numpy.fabs(nos.phi() - o.phi(ts[-2:])) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct phi"
assert (
not nos._roSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
assert (
not nos._voSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
return None
# Test new orbits formed from __call__, before integration
def test_newOrbit_b4integration():
from galpy.orbit import Orbit
o = Orbit([[1.0, 0.1, 1.1, 0.1, 0.0, 0.0], [1.1, 0.3, 0.9, -0.2, 0.3, 2.0]])
no = o() # New Orbits formed before integration
assert numpy.all(
numpy.fabs(no.R() - o.R()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct R"
assert numpy.all(
numpy.fabs(no.vR() - o.vR()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vR"
assert numpy.all(
numpy.fabs(no.vT() - o.vT()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vT"
assert numpy.all(
numpy.fabs(no.z() - o.z()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct z"
assert numpy.all(
numpy.fabs(no.vz() - o.vz()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct vz"
assert numpy.all(
numpy.fabs(no.phi() - o.phi()) < 10.0**-10.0
), "New Orbits formed from calling an old orbit does not have the correct phi"
assert (
not no._roSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
assert (
not no._voSet
), "New Orbits formed from calling an old orbit does not have the correct roSet"
return None
# Test that we can still get outputs when there aren't enough points for an actual interpolation
def test_badinterpolation():
from galpy.orbit import Orbit
o = Orbit([[1.0, 0.1, 1.1, 0.1, 0.0, 0.0], [1.1, 0.3, 0.9, -0.2, 0.3, 2.0]])
ts = numpy.linspace(
0.0, 1.0, 3
) # v. quick orbit integration, w/ not enough points for interpolation
lp = potential.LogarithmicHaloPotential(normalize=1.0)
o.integrate(ts, lp)
no = o(ts[-1]) # new orbit
assert numpy.all(
no.R() == o.R(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct R"
assert numpy.all(
no.vR() == o.vR(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct vR"
assert numpy.all(
no.vT() == o.vT(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct vT"
assert numpy.all(
no.z() == o.z(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct z"
assert numpy.all(
no.vz() == o.vz(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct vz"
assert numpy.all(
no.phi() == o.phi(ts[-1])
), "New orbit formed from calling an old orbit does not have the correct phi"
assert (
not no._roSet
), "New orbit formed from calling an old orbit does not have the correct roSet"
assert (
not no._voSet
), "New orbit formed from calling an old orbit does not have the correct roSet"
# Also test this for multiple time outputs
nos = o(ts[-2:]) # new Orbits
# First t
assert numpy.all(
numpy.fabs(nos.R() - o.R(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct R"
assert numpy.all(
numpy.fabs(nos.vR() - o.vR(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct vR"
assert numpy.all(
numpy.fabs(nos.vT() - o.vT(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct vT"
assert numpy.all(
numpy.fabs(nos.z() - o.z(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct z"
assert numpy.all(
numpy.fabs(nos.vz() - o.vz(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct vz"
assert numpy.all(
numpy.fabs(nos.phi() - o.phi(ts[-2:])) < 10.0**-10.0
), "New orbit formed from calling an old orbit does not have the correct phi"
assert (
not nos._roSet
), "New orbit formed from calling an old orbit does not have the correct roSet"
assert (
not nos._voSet
), "New orbit formed from calling an old orbit does not have the correct roSet"
# Try point in between, shouldn't work
with pytest.raises(LookupError) as exc_info:
no = o(0.6)
return None
# Check plotting routines
def test_plotting():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
o = Orbit(
[Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0]), Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0])]
)
oa = Orbit([Orbit([1.0, 0.1, 1.1, 0.1, 0.2]), Orbit([1.0, 0.1, 1.1, 0.1, 0.2])])
# Interesting shape
os = Orbit(
numpy.array(
[
[[1.0, 0.1, 1.1, -0.1, -0.2, 0.0], [1.0, 0.2, 1.2, 0.0, -0.1, 1.0]],
[[1.0, -0.2, 0.9, 0.2, 0.2, 2.0], [1.2, -0.4, 1.1, -0.1, 0.0, -2.0]],
[[1.0, 0.2, 0.9, 0.3, -0.2, 0.1], [1.2, 0.4, 1.1, -0.2, 0.05, 4.0]],
]
)
)
times = numpy.linspace(0.0, 7.0, 251)
lp = LogarithmicHaloPotential(normalize=1.0, q=0.8)
# Integrate
o.integrate(times, lp)
oa.integrate(times, lp)
os.integrate(times, lp)
# Some plots
# Energy
o.plotE()
o.plotE(normed=True)
o.plotE(pot=lp, d1="R")
o.plotE(pot=lp, d1="vR")
o.plotE(pot=lp, d1="vT")
o.plotE(pot=lp, d1="z")
o.plotE(pot=lp, d1="vz")
o.plotE(pot=lp, d1="phi")
oa.plotE()
oa.plotE(pot=lp, d1="R")
oa.plotE(pot=lp, d1="vR")
oa.plotE(pot=lp, d1="vT")
oa.plotE(pot=lp, d1="z")
oa.plotE(pot=lp, d1="vz")
os.plotE()
os.plotE(pot=lp, d1="R")
os.plotE(pot=lp, d1="vR")
os.plotE(pot=lp, d1="vT")
os.plotE(pot=lp, d1="z")
os.plotE(pot=lp, d1="vz")
# Vertical energy
o.plotEz()
o.plotEz(normed=True)
o.plotEz(pot=lp, d1="R")
o.plotEz(pot=lp, d1="vR")
o.plotEz(pot=lp, d1="vT")
o.plotEz(pot=lp, d1="z")
o.plotEz(pot=lp, d1="vz")
o.plotEz(pot=lp, d1="phi")
oa.plotEz()
oa.plotEz(normed=True)
oa.plotEz(pot=lp, d1="R")
oa.plotEz(pot=lp, d1="vR")
oa.plotEz(pot=lp, d1="vT")
oa.plotEz(pot=lp, d1="z")
oa.plotEz(pot=lp, d1="vz")
os.plotEz()
os.plotEz(normed=True)
os.plotEz(pot=lp, d1="R")
os.plotEz(pot=lp, d1="vR")
os.plotEz(pot=lp, d1="vT")
os.plotEz(pot=lp, d1="z")
os.plotEz(pot=lp, d1="vz")
# Radial energy
o.plotER()
o.plotER(normed=True)
# Radial energy
oa.plotER()
oa.plotER(normed=True)
os.plotER()
os.plotER(normed=True)
# Jacobi
o.plotJacobi()
o.plotJacobi(normed=True)
o.plotJacobi(pot=lp, d1="R", OmegaP=1.0)
o.plotJacobi(pot=lp, d1="vR")
o.plotJacobi(pot=lp, d1="vT")
o.plotJacobi(pot=lp, d1="z")
o.plotJacobi(pot=lp, d1="vz")
o.plotJacobi(pot=lp, d1="phi")
oa.plotJacobi()
oa.plotJacobi(pot=lp, d1="R", OmegaP=1.0)
oa.plotJacobi(pot=lp, d1="vR")
oa.plotJacobi(pot=lp, d1="vT")
oa.plotJacobi(pot=lp, d1="z")
oa.plotJacobi(pot=lp, d1="vz")
os.plotJacobi()
os.plotJacobi(pot=lp, d1="R", OmegaP=1.0)
os.plotJacobi(pot=lp, d1="vR")
os.plotJacobi(pot=lp, d1="vT")
os.plotJacobi(pot=lp, d1="z")
os.plotJacobi(pot=lp, d1="vz")
# Plot the orbit itself
o.plot() # defaults
oa.plot()
os.plot()
o.plot(d1="vR")
o.plotR()
o.plotvR(d1="vT")
o.plotvT(d1="z")
o.plotz(d1="vz")
o.plotvz(d1="phi")
o.plotphi(d1="vR")
o.plotx(d1="vx")
o.plotvx(d1="y")
o.ploty(d1="vy")
o.plotvy(d1="x")
# Remaining attributes
o.plot(d1="ra", d2="dec")
o.plot(d2="ra", d1="dec")
o.plot(d1="pmra", d2="pmdec")
o.plot(d2="pmra", d1="pmdec")
o.plot(d1="ll", d2="bb")
o.plot(d2="ll", d1="bb")
o.plot(d1="pmll", d2="pmbb")
o.plot(d2="pmll", d1="pmbb")
o.plot(d1="vlos", d2="dist")
o.plot(d2="vlos", d1="dist")
o.plot(d1="helioX", d2="U")
o.plot(d2="helioX", d1="U")
o.plot(d1="helioY", d2="V")
o.plot(d2="helioY", d1="V")
o.plot(d1="helioZ", d2="W")
o.plot(d2="helioZ", d1="W")
o.plot(d2="r", d1="R")
o.plot(d2="R", d1="r")
# Some more energies etc.
o.plot(d1="E", d2="R")
o.plot(d1="Enorm", d2="R")
o.plot(d1="Ez", d2="R")
o.plot(d1="Eznorm", d2="R")
o.plot(d1="ER", d2="R")
o.plot(d1="ERnorm", d2="R")
o.plot(d1="Jacobi", d2="R")
o.plot(d1="Jacobinorm", d2="R")
# callables
o.plot(d1=lambda t: t, d2=lambda t: o.R(t))
# Expressions
o.plot(d1="t", d2="r*R/vR")
os.plot(d1="t", d2="r*R/vR")
return None
def test_plotSOS():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
# 3D
o = Orbit(
[Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0]), Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0])]
)
pot = potential.MWPotential2014
o.plotSOS(pot)
o.plotSOS(pot, use_physical=True, ro=8.0, vo=220.0)
# 2D
o = Orbit([Orbit([1.0, 0.1, 1.1, 2.0]), Orbit([1.0, 0.1, 1.1, 2.0])])
pot = LogarithmicHaloPotential(normalize=1.0).toPlanar()
o.plotSOS(pot)
o.plotSOS(pot, use_physical=True, ro=8.0, vo=220.0)
return None
def test_plotBruteSOS():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
# 3D
o = Orbit(
[Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0]), Orbit([1.0, 0.1, 1.1, 0.1, 0.2, 2.0])]
)
pot = potential.MWPotential2014
o.plotBruteSOS(numpy.linspace(0.0, 20.0 * numpy.pi, 100001), pot)
o.plotBruteSOS(
numpy.linspace(0.0, 20.0 * numpy.pi, 100001),
pot,
use_physical=True,
ro=8.0,
vo=220.0,
)
# 2D
o = Orbit([Orbit([1.0, 0.1, 1.1, 2.0]), Orbit([1.0, 0.1, 1.1, 2.0])])
pot = LogarithmicHaloPotential(normalize=1.0).toPlanar()
o.plotBruteSOS(numpy.linspace(0.0, 20.0 * numpy.pi, 100001), pot)
o.plotBruteSOS(
numpy.linspace(0.0, 20.0 * numpy.pi, 100001),
pot,
use_physical=True,
ro=8.0,
vo=220.0,
)
return None
def test_integrate_method_warning():
"""Test Orbits.integrate raises an error if method is invalid"""
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
o = Orbit(
[
Orbit(vxvv=[1.0, 0.1, 0.1, 0.5, 0.1, 0.0]),
Orbit(vxvv=[1.0, 0.1, 0.1, 0.5, 0.1, 0.0]),
]
)
t = numpy.arange(0.0, 10.0, 0.001)
with pytest.raises(ValueError):
o.integrate(t, MWPotential2014, method="rk4")
# Test that fallback onto Python integrators works for Orbits
def test_integrate_Cfallback_symplec():
from test_potential import BurkertPotentialNoC
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0]),
Orbit([0.9, 0.3, 1.0]),
Orbit([1.2, -0.3, 0.7]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits
pot = BurkertPotentialNoC()
pot.normalize(1.0)
orbits.integrate(times, pot, method="symplec4_c")
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, pot, method="symplec4_c")
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
def test_integrate_Cfallback_nonsymplec():
from test_potential import BurkertPotentialNoC
from galpy.orbit import Orbit
times = numpy.linspace(0.0, 10.0, 1001)
orbits_list = [
Orbit([1.0, 0.1, 1.0]),
Orbit([0.9, 0.3, 1.0]),
Orbit([1.2, -0.3, 0.7]),
]
orbits = Orbit(orbits_list)
# Integrate as Orbits
pot = BurkertPotentialNoC()
pot.normalize(1.0)
orbits.integrate(times, pot, method="dop853_c")
# Integrate as multiple Orbits
for o in orbits_list:
o.integrate(times, pot, method="dop853_c")
# Compare
for ii in range(len(orbits)):
assert (
numpy.amax(numpy.fabs(orbits_list[ii].R(times) - orbits.R(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vR(times) - orbits.vR(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
assert (
numpy.amax(numpy.fabs(orbits_list[ii].vT(times) - orbits.vT(times)[ii]))
< 1e-10
), "Integration of multiple orbits as Orbits does not agree with integrating multiple orbits"
return None
# Test flippingg an orbit
def setup_orbits_flip(tp, ro, vo, zo, solarmotion, axi=False):
from galpy.orbit import Orbit
if isinstance(tp, potential.linearPotential):
o = Orbit(
[[1.0, 1.0], [0.2, -0.3]], ro=ro, vo=vo, zo=zo, solarmotion=solarmotion
)
elif isinstance(tp, potential.planarPotential):
if axi:
o = Orbit(
[[1.0, 1.1, 1.1], [1.1, -0.1, 0.9]],
ro=ro,
vo=vo,
zo=zo,
solarmotion=solarmotion,
)
else:
o = Orbit(
[[1.0, 1.1, 1.1, 0.0], [1.1, -1.2, -0.9, 2.0]],
ro=ro,
vo=vo,
zo=zo,
solarmotion=solarmotion,
)
else:
if axi:
o = Orbit(
[[1.0, 1.1, 1.1, 0.1, 0.1], [1.1, -0.7, 1.4, -0.1, 0.3]],
ro=ro,
vo=vo,
zo=zo,
solarmotion=solarmotion,
)
else:
o = Orbit(
[[1.0, 1.1, 1.1, 0.1, 0.1, 0.0], [0.6, -0.4, -1.0, -0.3, -0.5, 2.0]],
ro=ro,
vo=vo,
zo=zo,
solarmotion=solarmotion,
)
return o
def test_flip():
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
plp = lp.toPlanar()
llp = lp.toVertical(1.0)
for ii in range(5):
# Scales to test that these are properly propagated to the new Orbit
ro, vo, zo, solarmotion = 10.0, 300.0, 0.01, "schoenrich"
if ii == 0: # axi, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=True)
elif ii == 1: # track azimuth, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=False)
elif ii == 2: # axi, planar
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=True)
elif ii == 3: # track azimuth, full
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=False)
elif ii == 4: # linear orbit
o = setup_orbits_flip(llp, ro, vo, None, None, axi=False)
of = o.flip()
# First check that the scales have been propagated properly
assert (
numpy.fabs(o._ro - of._ro) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
numpy.fabs(o._vo - of._vo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert (
(o._zo is None) * (of._zo is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
(o._solarmotion is None) * (of._solarmotion is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
else:
assert (
numpy.fabs(o._zo - of._zo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert numpy.all(
numpy.fabs(o._solarmotion - of._solarmotion) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._roSet == of._roSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._voSet == of._voSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert numpy.all(
numpy.abs(o.x() - of.x()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vx() + of.vx()) < 10.0**-10.0
), "o.flip() did not work as expected"
else:
assert numpy.all(
numpy.abs(o.R() - of.R()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vR() + of.vR()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vT() + of.vT()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii % 2 == 1:
assert numpy.all(
numpy.abs(o.phi() - of.phi()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii < 2:
assert numpy.all(
numpy.abs(o.z() - of.z()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vz() + of.vz()) < 10.0**-10.0
), "o.flip() did not work as expected"
return None
# Test flippingg an orbit inplace
def test_flip_inplace():
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
plp = lp.toPlanar()
llp = lp.toVertical(1.0)
for ii in range(5):
# Scales (not really necessary for this test)
ro, vo, zo, solarmotion = 10.0, 300.0, 0.01, "schoenrich"
if ii == 0: # axi, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=True)
elif ii == 1: # track azimuth, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=False)
elif ii == 2: # axi, planar
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=True)
elif ii == 3: # track azimuth, full
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=False)
elif ii == 4: # linear orbit
o = setup_orbits_flip(llp, ro, vo, None, None, axi=False)
of = o()
of.flip(inplace=True)
# First check that the scales have been propagated properly
assert (
numpy.fabs(o._ro - of._ro) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
numpy.fabs(o._vo - of._vo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert (
(o._zo is None) * (of._zo is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
(o._solarmotion is None) * (of._solarmotion is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
else:
assert (
numpy.fabs(o._zo - of._zo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert numpy.all(
numpy.fabs(o._solarmotion - of._solarmotion) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._roSet == of._roSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._voSet == of._voSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert numpy.all(
numpy.abs(o.x() - of.x()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vx() + of.vx()) < 10.0**-10.0
), "o.flip() did not work as expected"
else:
assert numpy.all(
numpy.abs(o.R() - of.R()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vR() + of.vR()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vT() + of.vT()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii % 2 == 1:
assert numpy.all(
numpy.abs(o.phi() - of.phi()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii < 2:
assert numpy.all(
numpy.abs(o.z() - of.z()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vz() + of.vz()) < 10.0**-10.0
), "o.flip() did not work as expected"
return None
# Test flippingg an orbit inplace after orbit integration
def test_flip_inplace_integrated():
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
plp = lp.toPlanar()
llp = lp.toVertical(1.0)
ts = numpy.linspace(0.0, 1.0, 11)
for ii in range(5):
# Scales (not really necessary for this test)
ro, vo, zo, solarmotion = 10.0, 300.0, 0.01, "schoenrich"
if ii == 0: # axi, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=True)
elif ii == 1: # track azimuth, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=False)
elif ii == 2: # axi, planar
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=True)
elif ii == 3: # track azimuth, full
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=False)
elif ii == 4: # linear orbit
o = setup_orbits_flip(llp, ro, vo, None, None, axi=False)
of = o()
if ii < 2 or ii == 3:
o.integrate(ts, lp)
of.integrate(ts, lp)
elif ii == 2:
o.integrate(ts, plp)
of.integrate(ts, plp)
else:
o.integrate(ts, llp)
of.integrate(ts, llp)
of.flip(inplace=True)
# Just check one time, allows code duplication!
o = o(0.5)
of = of(0.5)
# First check that the scales have been propagated properly
assert (
numpy.fabs(o._ro - of._ro) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
numpy.fabs(o._vo - of._vo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert (
(o._zo is None) * (of._zo is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
(o._solarmotion is None) * (of._solarmotion is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
else:
assert (
numpy.fabs(o._zo - of._zo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert numpy.all(
numpy.fabs(o._solarmotion - of._solarmotion) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._roSet == of._roSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._voSet == of._voSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert numpy.all(
numpy.abs(o.x() - of.x()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vx() + of.vx()) < 10.0**-10.0
), "o.flip() did not work as expected"
else:
assert numpy.all(
numpy.abs(o.R() - of.R()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vR() + of.vR()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vT() + of.vT()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii % 2 == 1:
assert numpy.all(
numpy.abs(o.phi() - of.phi()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii < 2:
assert numpy.all(
numpy.abs(o.z() - of.z()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vz() + of.vz()) < 10.0**-10.0
), "o.flip() did not work as expected"
return None
# Test flippingg an orbit inplace after orbit integration, and after having
# once evaluated the orbit before flipping inplace (#345)
# only difference wrt previous test is a line that evaluates of before
# flipping
def test_flip_inplace_integrated_evaluated():
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
plp = lp.toPlanar()
llp = lp.toVertical(1.0)
ts = numpy.linspace(0.0, 1.0, 11)
for ii in range(5):
# Scales (not really necessary for this test)
ro, vo, zo, solarmotion = 10.0, 300.0, 0.01, "schoenrich"
if ii == 0: # axi, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=True)
elif ii == 1: # track azimuth, full
o = setup_orbits_flip(lp, ro, vo, zo, solarmotion, axi=False)
elif ii == 2: # axi, planar
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=True)
elif ii == 3: # track azimuth, full
o = setup_orbits_flip(plp, ro, vo, zo, solarmotion, axi=False)
elif ii == 4: # linear orbit
o = setup_orbits_flip(llp, ro, vo, None, None, axi=False)
of = o()
if ii < 2 or ii == 3:
o.integrate(ts, lp)
of.integrate(ts, lp)
elif ii == 2:
o.integrate(ts, plp)
of.integrate(ts, plp)
else:
o.integrate(ts, llp)
of.integrate(ts, llp)
# Evaluate, make sure it is at an interpolated time!
dumb = of.R(0.52)
# Now flip
of.flip(inplace=True)
# Just check one time, allows code duplication!
o = o(0.52)
of = of(0.52)
# First check that the scales have been propagated properly
assert (
numpy.fabs(o._ro - of._ro) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
numpy.fabs(o._vo - of._vo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert (
(o._zo is None) * (of._zo is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
(o._solarmotion is None) * (of._solarmotion is None)
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
else:
assert (
numpy.fabs(o._zo - of._zo) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert numpy.all(
numpy.fabs(o._solarmotion - of._solarmotion) < 10.0**-15.0
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._roSet == of._roSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
assert (
o._voSet == of._voSet
), "o.flip() did not conserve physical scales and coordinate-transformation parameters"
if ii == 4:
assert numpy.all(
numpy.abs(o.x() - of.x()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vx() + of.vx()) < 10.0**-10.0
), "o.flip() did not work as expected"
else:
assert numpy.all(
numpy.abs(o.R() - of.R()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vR() + of.vR()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vT() + of.vT()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii % 2 == 1:
assert numpy.all(
numpy.abs(o.phi() - of.phi()) < 10.0**-10.0
), "o.flip() did not work as expected"
if ii < 2:
assert numpy.all(
numpy.abs(o.z() - of.z()) < 10.0**-10.0
), "o.flip() did not work as expected"
assert numpy.all(
numpy.abs(o.vz() + of.vz()) < 10.0**-10.0
), "o.flip() did not work as expected"
return None
# test getOrbit
def test_getOrbit():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0, q=0.9)
o = Orbit([[1.0, 0.1, 1.2, 0.3, 0.2, 2.0], [1.0, -0.1, 1.1, -0.3, 0.2, 5.0]])
times = numpy.linspace(0.0, 7.0, 251)
o.integrate(times, lp)
Rs = o.R(times)
vRs = o.vR(times)
vTs = o.vT(times)
zs = o.z(times)
vzs = o.vz(times)
phis = o.phi(times)
orbarray = o.getOrbit()
assert (
numpy.all(numpy.fabs(Rs - orbarray[..., 0])) < 10.0**-16.0
), "getOrbit does not work as expected for R"
assert (
numpy.all(numpy.fabs(vRs - orbarray[..., 1])) < 10.0**-16.0
), "getOrbit does not work as expected for vR"
assert (
numpy.all(numpy.fabs(vTs - orbarray[..., 2])) < 10.0**-16.0
), "getOrbit does not work as expected for vT"
assert (
numpy.all(numpy.fabs(zs - orbarray[..., 3])) < 10.0**-16.0
), "getOrbit does not work as expected for z"
assert (
numpy.all(numpy.fabs(vzs - orbarray[..., 4])) < 10.0**-16.0
), "getOrbit does not work as expected for vz"
assert (
numpy.all(numpy.fabs(phis - orbarray[..., 5])) < 10.0**-16.0
), "getOrbit does not work as expected for phi"
return None
# Test that the eccentricity, zmax, rperi, and rap calculated numerically by
# Orbits agrees with that calculated numerically using Orbit
def test_EccZmaxRperiRap_num_againstorbit_3d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test AttributeError when not integrated
with pytest.raises(AttributeError):
os.e()
with pytest.raises(AttributeError):
os.zmax()
with pytest.raises(AttributeError):
os.rperi()
with pytest.raises(AttributeError):
os.rap()
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.e()[ii] - list_os[ii].e()) < 1e-10
), "Evaluating Orbits e does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.zmax()[ii] - list_os[ii].zmax()) < 1e-10
), "Evaluating Orbits zmax does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.rperi()[ii] - list_os[ii].rperi()) < 1e-10
), "Evaluating Orbits rperi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.rap()[ii] - list_os[ii].rap()) < 1e-10
), "Evaluating Orbits rap does not agree with Orbit"
return None
def test_EccZmaxRperiRap_num_againstorbit_2d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, phis)))
list_os = [Orbit([R, vR, vT, phi]) for R, vR, vT, phi in zip(Rs, vRs, vTs, phis)]
# Integrate all
times = numpy.linspace(0.0, 10.0, 1001)
os.integrate(times, MWPotential2014)
[o.integrate(times, MWPotential2014) for o in list_os]
for ii in range(nrand):
assert numpy.all(
numpy.fabs(os.e()[ii] - list_os[ii].e()) < 1e-10
), "Evaluating Orbits e does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.rperi()[ii] - list_os[ii].rperi()) < 1e-10
), "Evaluating Orbits rperi does not agree with Orbit"
assert numpy.all(
numpy.fabs(os.rap()[ii] - list_os[ii].rap()) < 1e-10
), "Evaluating Orbits rap does not agree with Orbit"
return None
# Test that the eccentricity, zmax, rperi, and rap calculated analytically by
# Orbits agrees with that calculated analytically using Orbit
def test_EccZmaxRperiRap_analytic_againstorbit_3d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test AttributeError when no potential and not integrated
with pytest.raises(AttributeError):
os.e(analytic=True)
with pytest.raises(AttributeError):
os.zmax(analytic=True)
with pytest.raises(AttributeError):
os.rperi(analytic=True)
with pytest.raises(AttributeError):
os.rap(analytic=True)
for type in ["spherical", "staeckel", "adiabatic"]:
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.e(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].e(pot=MWPotential2014, analytic=True, type=type)
)
< 1e-10
), f"Evaluating Orbits e analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.zmax(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].zmax(pot=MWPotential2014, analytic=True, type=type)
)
< 1e-10
), f"Evaluating Orbits zmax analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.rperi(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].rperi(pot=MWPotential2014, analytic=True, type=type)
)
< 1e-10
), f"Evaluating Orbits rperi analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.rap(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].rap(pot=MWPotential2014, analytic=True, type=type)
)
< 1e-10
), f"Evaluating Orbits rap analytically does not agree with Orbit for type={type}"
return None
def test_EccZmaxRperiRap_analytic_againstorbit_2d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, phis)))
list_os = [Orbit([R, vR, vT, phi]) for R, vR, vT, phi in zip(Rs, vRs, vTs, phis)]
# No matter the type, should always be using adiabtic, not specified in
# Orbit
for type in ["spherical", "staeckel", "adiabatic"]:
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.e(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].e(pot=MWPotential2014, analytic=True)
)
< 1e-10
), f"Evaluating Orbits e analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.rperi(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].rperi(pot=MWPotential2014, analytic=True, type=type)
)
< 1e-10
), f"Evaluating Orbits rperi analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.rap(pot=MWPotential2014, analytic=True, type=type)[ii]
- list_os[ii].rap(pot=MWPotential2014, analytic=True)
)
< 1e-10
), f"Evaluating Orbits rap analytically does not agree with Orbit for type={type}"
return None
def test_rguiding():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test that if potential is not given, error is raised
with pytest.raises(RuntimeError):
os.rguiding()
# With small number, calculation is direct
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.rguiding(pot=MWPotential2014)[ii]
/ list_os[ii].rguiding(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits rguiding analytically does not agree with Orbit"
# With large number, calculation is interpolated
nrand = 1002
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
rgs = os.rguiding(pot=MWPotential2014)
for ii in range(nrand):
assert numpy.all(
numpy.fabs(rgs[ii] / list_os[ii].rguiding(pot=MWPotential2014) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits rguiding analytically does not agree with Orbit"
# rguiding for non-axi potential fails
with pytest.raises(
RuntimeError,
match="Potential given to rguiding is non-axisymmetric, but rguiding requires an axisymmetric potential",
) as exc_info:
os.rguiding(pot=MWPotential2014 + potential.DehnenBarPotential())
return None
def test_rE():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test that if potential is not given, error is raised
with pytest.raises(RuntimeError):
os.rE()
# With small number, calculation is direct
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.rE(pot=MWPotential2014)[ii] / list_os[ii].rE(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits rE analytically does not agree with Orbit"
# With large number, calculation is interpolated
nrand = 1002
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
rgs = os.rE(pot=MWPotential2014)
for ii in range(nrand):
assert numpy.all(
numpy.fabs(rgs[ii] / list_os[ii].rE(pot=MWPotential2014) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits rE analytically does not agree with Orbit"
# rE for non-axi potential fails
with pytest.raises(
RuntimeError,
match="Potential given to rE is non-axisymmetric, but rE requires an axisymmetric potential",
) as exc_info:
os.rE(pot=MWPotential2014 + potential.DehnenBarPotential())
return None
def test_LcE():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test that if potential is not given, error is raised
with pytest.raises(RuntimeError):
os.LcE()
# With small number, calculation is direct
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.LcE(pot=MWPotential2014)[ii] / list_os[ii].LcE(pot=MWPotential2014)
- 1.0
)
< 10.0**-10.0
), "Evaluating Orbits LcE analytically does not agree with Orbit"
# With large number, calculation is interpolated
nrand = 1002
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
rgs = os.LcE(pot=MWPotential2014)
for ii in range(nrand):
assert numpy.all(
numpy.fabs(rgs[ii] / list_os[ii].LcE(pot=MWPotential2014) - 1.0)
< 10.0**-10.0
), "Evaluating Orbits LcE analytically does not agree with Orbit"
# LcE for non-axi potential fails
with pytest.raises(
RuntimeError,
match="Potential given to LcE is non-axisymmetric, but LcE requires an axisymmetric potential",
) as exc_info:
os.LcE(pot=MWPotential2014 + potential.DehnenBarPotential())
return None
# Test that the actions, frequencies/periods, and angles calculated
# analytically by Orbits agrees with that calculated analytically using Orbit
def test_actionsFreqsAngles_againstorbit_3d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, zs, vzs, phis)))
list_os = [
Orbit([R, vR, vT, z, vz, phi])
for R, vR, vT, z, vz, phi in zip(Rs, vRs, vTs, zs, vzs, phis)
]
# First test AttributeError when no potential and not integrated
with pytest.raises(AttributeError):
os.jr()
with pytest.raises(AttributeError):
os.jp()
with pytest.raises(AttributeError):
os.jz()
with pytest.raises(AttributeError):
os.wr()
with pytest.raises(AttributeError):
os.wp()
with pytest.raises(AttributeError):
os.wz()
with pytest.raises(AttributeError):
os.Or()
with pytest.raises(AttributeError):
os.Op()
with pytest.raises(AttributeError):
os.Oz()
with pytest.raises(AttributeError):
os.Tr()
with pytest.raises(AttributeError):
os.Tp()
with pytest.raises(AttributeError):
os.TrTp()
with pytest.raises(AttributeError):
os.Tz()
# Tolerance for jr, jp, jz, diff. for isochroneApprox, because currently
# not implemented in exactly the same way in Orbit and Orbits (Orbit uses
# __call__ for the actions, Orbits uses actionsFreqsAngles, which is diff.)
tol = {}
tol["spherical"] = -12.0
tol["staeckel"] = -12.0
tol["adiabatic"] = -12.0
tol["isochroneApprox"] = -2.0
# For now we skip adiabatic here, because frequencies and angles not
# implemented yet
# for type in ['spherical','staeckel','adiabatic']:
for type in ["spherical", "staeckel", "isochroneApprox"]:
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.jr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].jr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.jp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].jp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.jz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].jz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.wr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].wr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.wp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].wp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.wz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].wz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Or(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Or(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Or analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Op(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Op(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Op analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Oz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Oz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Oz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.TrTp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].TrTp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits TrTp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tz analytically does not agree with Orbit for type={type}"
if type == "isochroneApprox":
break # otherwise takes too long
return None
# Test that the actions, frequencies/periods, and angles calculated
# analytically by Orbits agrees with that calculated analytically using Orbit
def test_actionsFreqsAngles_againstorbit_2d():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = 10
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
os = Orbit(list(zip(Rs, vRs, vTs, phis)))
list_os = [Orbit([R, vR, vT, phi]) for R, vR, vT, phi in zip(Rs, vRs, vTs, phis)]
# First test AttributeError when no potential and not integrated
with pytest.raises(AttributeError):
os.jr()
with pytest.raises(AttributeError):
os.jp()
with pytest.raises(AttributeError):
os.jz()
with pytest.raises(AttributeError):
os.wr()
with pytest.raises(AttributeError):
os.wp()
with pytest.raises(AttributeError):
os.wz()
with pytest.raises(AttributeError):
os.Or()
with pytest.raises(AttributeError):
os.Op()
with pytest.raises(AttributeError):
os.Oz()
with pytest.raises(AttributeError):
os.Tr()
with pytest.raises(AttributeError):
os.Tp()
with pytest.raises(AttributeError):
os.TrTp()
with pytest.raises(AttributeError):
os.Tz()
# Tolerance for jr, jp, jz, diff. for isochroneApprox, because currently
# not implemented in exactly the same way in Orbit and Orbits (Orbit uses
# __call__ for the actions, Orbits uses actionsFreqsAngles, which is diff.)
tol = {}
tol["spherical"] = -12.0
tol["staeckel"] = -12.0
tol["adiabatic"] = -12.0
tol["isochroneApprox"] = -2.0
# For now we skip adiabatic here, because frequencies and angles not
# implemented yet
# for type in ['spherical','staeckel','adiabatic']:
for type in ["spherical", "staeckel", "isochroneApprox"]:
for ii in range(nrand):
assert numpy.all(
numpy.fabs(
os.jr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].jr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.jp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].jp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jp analytically does not agree with Orbit for type={type}"
# zero, so don't divide, also doesn't work for isochroneapprox now
if not type == "isochroneApprox":
assert numpy.all(
numpy.fabs(
os.jz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
- list_os[ii].jz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.wr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].wr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wr analytically does not agree with Orbit for type={type}"
# Think I may have fixed wp = NaN?
# assert numpy.all(numpy.fabs(os.wp(pot=MWPotential2014,analytic=True,type=type,b=0.8)[ii]/list_os[ii].wp(pot=MWPotential2014,analytic=True,type=type,b=0.8)-1.) < 1e-10), 'Evaluating Orbits wp analytically does not agree with Orbit for type={}'.format(type)
# assert numpy.all(numpy.fabs(os.wz(pot=MWPotential2014,analytic=True,type=type,b=0.8)[ii]/list_os[ii].wz(pot=MWPotential2014,analytic=True,type=type,b=0.8)-1.) < 1e-10), 'Evaluating Orbits wz analytically does not agree with Orbit for type={}'.format(type)
assert numpy.all(
numpy.fabs(
os.Or(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Or(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Or analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Op(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Op(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Op analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Oz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Oz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Oz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tr(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.TrTp(pot=MWPotential2014, analytic=True, type=type, b=0.8)[
ii
]
/ list_os[ii].TrTp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits TrTp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
os.Tz(pot=MWPotential2014, analytic=True, type=type, b=0.8)[ii]
/ list_os[ii].Tz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tz analytically does not agree with Orbit for type={type}"
if type == "isochroneApprox":
break # otherwise takes too long
return None
def test_actionsFreqsAngles_output_shape():
# Test that the output shape is correct and that the shaped output is correct for actionAngle methods
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
numpy.random.seed(1)
nrand = (3, 1, 2)
Rs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
vRs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vTs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0) + 1.0
zs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vzs = 0.2 * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
phis = 2.0 * numpy.pi * (2.0 * numpy.random.uniform(size=nrand) - 1.0)
vxvv = numpy.rollaxis(numpy.array([Rs, vRs, vTs, zs, vzs, phis]), 0, 4)
os = Orbit(vxvv)
list_os = [
[
[
Orbit(
[
Rs[ii, jj, kk],
vRs[ii, jj, kk],
vTs[ii, jj, kk],
zs[ii, jj, kk],
vzs[ii, jj, kk],
phis[ii, jj, kk],
]
)
for kk in range(nrand[2])
]
for jj in range(nrand[1])
]
for ii in range(nrand[0])
]
# Tolerance for jr, jp, jz, diff. for isochroneApprox, because currently
# not implemented in exactly the same way in Orbit and Orbits (Orbit uses
# __call__ for the actions, Orbits uses actionsFreqsAngles, which is diff.)
tol = {}
tol["spherical"] = -12.0
tol["staeckel"] = -12.0
tol["adiabatic"] = -12.0
tol["isochroneApprox"] = -2.0
# For now we skip adiabatic here, because frequencies and angles not
# implemented yet
# for type in ['spherical','staeckel','adiabatic']:
for type in ["spherical", "staeckel", "isochroneApprox"]:
# Evaluate Orbits once to not be too slow...
tjr = os.jr(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tjp = os.jp(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tjz = os.jz(pot=MWPotential2014, analytic=True, type=type, b=0.8)
twr = os.wr(pot=MWPotential2014, analytic=True, type=type, b=0.8)
twp = os.wp(pot=MWPotential2014, analytic=True, type=type, b=0.8)
twz = os.wz(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tOr = os.Or(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tOp = os.Op(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tOz = os.Oz(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tTr = os.Tr(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tTp = os.Tp(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tTrTp = os.TrTp(pot=MWPotential2014, analytic=True, type=type, b=0.8)
tTz = os.Tz(pot=MWPotential2014, analytic=True, type=type, b=0.8)
for ii in range(nrand[0]):
for jj in range(nrand[1]):
for kk in range(nrand[2]):
assert numpy.all(
numpy.fabs(
tjr[ii, jj, kk]
/ list_os[ii][jj][kk].jr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tjp[ii, jj, kk]
/ list_os[ii][jj][kk].jp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tjz[ii, jj, kk]
/ list_os[ii][jj][kk].jz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 10.0 ** tol[type]
), f"Evaluating Orbits jz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
twr[ii, jj, kk]
/ list_os[ii][jj][kk].wr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
twp[ii, jj, kk]
/ list_os[ii][jj][kk].wp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
twz[ii, jj, kk]
/ list_os[ii][jj][kk].wz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits wz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tOr[ii, jj, kk]
/ list_os[ii][jj][kk].Or(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Or analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tOp[ii, jj, kk]
/ list_os[ii][jj][kk].Op(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Op analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tOz[ii, jj, kk]
/ list_os[ii][jj][kk].Oz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Oz analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tTr[ii, jj, kk]
/ list_os[ii][jj][kk].Tr(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tr analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tTp[ii, jj, kk]
/ list_os[ii][jj][kk].Tp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tTrTp[ii, jj, kk]
/ list_os[ii][jj][kk].TrTp(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits TrTp analytically does not agree with Orbit for type={type}"
assert numpy.all(
numpy.fabs(
tTz[ii, jj, kk]
/ list_os[ii][jj][kk].Tz(
pot=MWPotential2014, analytic=True, type=type, b=0.8
)
- 1.0
)
< 1e-10
), f"Evaluating Orbits Tz analytically does not agree with Orbit for type={type}"
if type == "isochroneApprox":
break # otherwise takes too long
return None
# Test that the delta parameter is properly dealt with when using the staeckel
# approximation: when it changes, need to re-do the aA calcs.
def test_actionsFreqsAngles_staeckeldelta():
from galpy.orbit import Orbit
from galpy.potential import MWPotential2014
os = Orbit([None, None]) # Just twice the Sun!
# First with delta
jr = os.jr(delta=0.4, pot=MWPotential2014)
# Now without, should be different
jrn = os.jr(pot=MWPotential2014)
assert numpy.all(
numpy.fabs(jr - jrn) > 1e-4
), "Action calculation in Orbits using Staeckel approximation not updated when going from specifying delta to not specifying it"
# Again, now the other way around
os = Orbit([None, None]) # Just twice the Sun!
# First without delta
jrn = os.jr(pot=MWPotential2014)
# Now with, should be different
jr = os.jr(delta=0.4, pot=MWPotential2014)
assert numpy.all(
numpy.fabs(jr - jrn) > 1e-4
), "Action calculation in Orbits using Staeckel approximation not updated when going from specifying delta to not specifying it"
return None
# Test that actionAngleStaeckel for a spherical potential is the same
# as actionAngleSpherical
def test_actionsFreqsAngles_staeckeldeltaequalzero():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
os = Orbit([None, None]) # Just twice the Sun!
lp = LogarithmicHaloPotential(normalize=1.0)
assert numpy.all(
numpy.fabs(os.jr(pot=lp, type="staeckel") - os.jr(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.jp(pot=lp, type="staeckel") - os.jp(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.jz(pot=lp, type="staeckel") - os.jz(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.wr(pot=lp, type="staeckel") - os.wr(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.wp(pot=lp, type="staeckel") - os.wp(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.wz(pot=lp, type="staeckel") - os.wz(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.Tr(pot=lp, type="staeckel") - os.Tr(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.Tp(pot=lp, type="staeckel") - os.Tp(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
assert numpy.all(
numpy.fabs(os.Tz(pot=lp, type="staeckel") - os.Tz(pot=lp, type="spherical"))
< 1e-8
), "Action-angle function for staeckel method with spherical potential is not equal to actionAngleSpherical"
return None
# Test that the b / ip parameters are properly dealt with when using the
# isochroneapprox approximation: when they change, need to re-do the aA calcs.
def test_actionsFreqsAngles_isochroneapproxb():
from galpy.orbit import Orbit
from galpy.potential import IsochronePotential, MWPotential2014
os = Orbit([None, None]) # Just twice the Sun!
# First with one b
jr = os.jr(type="isochroneapprox", b=0.8, pot=MWPotential2014)
# Now with another b, should be different
jrn = os.jr(type="isochroneapprox", b=1.8, pot=MWPotential2014)
assert numpy.all(
numpy.fabs(jr - jrn) > 1e-4
), "Action calculation in Orbits using isochroneapprox approximation not updated when going from specifying b to not specifying it"
# Again, now specifying ip
os = Orbit([None, None]) # Just twice the Sun!
# First with one
jrn = os.jr(
pot=MWPotential2014,
type="isochroneapprox",
ip=IsochronePotential(normalize=1.1, b=0.8),
)
# Now with another one, should be different
jr = os.jr(
pot=MWPotential2014,
type="isochroneapprox",
ip=IsochronePotential(normalize=0.99, b=1.8),
)
assert numpy.all(
numpy.fabs(jr - jrn) > 1e-4
), "Action calculation in Orbits using isochroneapprox approximation not updated when going from specifying delta to not specifying it"
return None
def test_actionsFreqsAngles_RuntimeError_1d():
from galpy.orbit import Orbit
os = Orbit([[1.0, 0.1], [0.2, 0.3]])
with pytest.raises(RuntimeError):
os.jz(analytic=True)
return None
def test_ChandrasekharDynamicalFrictionForce_constLambda():
# Test from test_potential for Orbits now!
#
# Test that the ChandrasekharDynamicalFrictionForce with constant Lambda
# agrees with analytical solutions for circular orbits:
# assuming that a mass remains on a circular orbit in an isothermal halo
# with velocity dispersion sigma and for constant Lambda:
# r_final^2 - r_initial^2 = -0.604 ln(Lambda) GM/sigma t
# (e.g., B&T08, p. 648)
from galpy.orbit import Orbit
from galpy.util import conversion
ro, vo = 8.0, 220.0
# Parameters
GMs = 10.0**9.0 / conversion.mass_in_msol(vo, ro)
const_lnLambda = 7.0
r_inits = [2.0, 2.5]
dt = 2.0 / conversion.time_in_Gyr(vo, ro)
# Compute
lp = potential.LogarithmicHaloPotential(normalize=1.0, q=1.0)
cdfc = potential.ChandrasekharDynamicalFrictionForce(
GMs=GMs, const_lnLambda=const_lnLambda, dens=lp
) # don't provide sigmar, so it gets computed using galpy.df.jeans
o = Orbit(
[
Orbit([r_inits[0], 0.0, 1.0, 0.0, 0.0, 0.0]),
Orbit([r_inits[1], 0.0, 1.0, 0.0, 0.0, 0.0]),
]
)
ts = numpy.linspace(0.0, dt, 1001)
o.integrate(ts, [lp, cdfc], method="leapfrog") # also tests fallback onto odeint
r_pred = numpy.sqrt(
numpy.array(o.r()) ** 2.0 - 0.604 * const_lnLambda * GMs * numpy.sqrt(2.0) * dt
)
assert numpy.all(
numpy.fabs(r_pred - numpy.array(o.r(ts[-1]))) < 0.015
), "ChandrasekharDynamicalFrictionForce with constant lnLambda for circular orbits does not agree with analytical prediction"
return None
# Check that toPlanar works
def test_toPlanar():
from galpy.orbit import Orbit
obs = Orbit([[1.0, 0.1, 1.1, 0.3, 0.0, 2.0], [1.0, -0.2, 1.3, -0.3, 0.0, 5.0]])
obsp = obs.toPlanar()
assert obsp.dim() == 2, "toPlanar does not generate an Orbit w/ dim=2 for FullOrbit"
assert numpy.all(
obsp.R() == obs.R()
), "Planar orbit generated w/ toPlanar does not have the correct R"
assert numpy.all(
obsp.vR() == obs.vR()
), "Planar orbit generated w/ toPlanar does not have the correct vR"
assert numpy.all(
obsp.vT() == obs.vT()
), "Planar orbit generated w/ toPlanar does not have the correct vT"
assert numpy.all(
obsp.phi() == obs.phi()
), "Planar orbit generated w/ toPlanar does not have the correct phi"
obs = Orbit([[1.0, 0.1, 1.1, 0.3, 0.0], [1.0, -0.2, 1.3, -0.3, 0.0]])
obsp = obs.toPlanar()
assert obsp.dim() == 2, "toPlanar does not generate an Orbit w/ dim=2 for RZOrbit"
assert numpy.all(
obsp.R() == obs.R()
), "Planar orbit generated w/ toPlanar does not have the correct R"
assert numpy.all(
obsp.vR() == obs.vR()
), "Planar orbit generated w/ toPlanar does not have the correct vR"
assert numpy.all(
obsp.vT() == obs.vT()
), "Planar orbit generated w/ toPlanar does not have the correct vT"
ro, vo, zo, solarmotion = 10.0, 300.0, 0.01, "schoenrich"
obs = Orbit(
[[1.0, 0.1, 1.1, 0.3, 0.0, 2.0], [1.0, -0.2, 1.3, -0.3, 0.0, 5.0]],
ro=ro,
vo=vo,
zo=zo,
solarmotion=solarmotion,
)
obsp = obs.toPlanar()
assert obsp.dim() == 2, "toPlanar does not generate an Orbit w/ dim=2 for RZOrbit"
assert numpy.all(
obsp.R() == obs.R()
), "Planar orbit generated w/ toPlanar does not have the correct R"
assert numpy.all(
obsp.vR() == obs.vR()
), "Planar orbit generated w/ toPlanar does not have the correct vR"
assert numpy.all(
obsp.vT() == obs.vT()
), "Planar orbit generated w/ toPlanar does not have the correct vT"
assert (
numpy.fabs(obs._ro - obsp._ro) < 10.0**-15.0
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
numpy.fabs(obs._vo - obsp._vo) < 10.0**-15.0
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
numpy.fabs(obs._zo - obsp._zo) < 10.0**-15.0
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert numpy.all(
numpy.fabs(obs._solarmotion - obsp._solarmotion) < 10.0**-15.0
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obs._roSet == obsp._roSet
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obs._voSet == obsp._voSet
), "Planar orbit generated w/ toPlanar does not have the proper physical scale and coordinate-transformation parameters associated with it"
obs = Orbit([[1.0, 0.1, 1.1, 0.3], [1.0, -0.2, 1.3, -0.3]])
try:
obs.toPlanar()
except AttributeError:
pass
else:
raise AttributeError(
"toPlanar() applied to a planar Orbit did not raise an AttributeError"
)
return None
# Check that toLinear works
def test_toLinear():
from galpy.orbit import Orbit
obs = Orbit([[1.0, 0.1, 1.1, 0.3, 0.0, 2.0], [1.0, -0.2, 1.3, -0.3, 0.0, 5.0]])
obsl = obs.toLinear()
assert obsl.dim() == 1, "toLinear does not generate an Orbit w/ dim=1 for FullOrbit"
assert numpy.all(
obsl.x() == obs.z()
), "Linear orbit generated w/ toLinear does not have the correct z"
assert numpy.all(
obsl.vx() == obs.vz()
), "Linear orbit generated w/ toLinear does not have the correct vx"
obs = Orbit([[1.0, 0.1, 1.1, 0.3, 0.0], [1.0, -0.2, 1.3, -0.3, 0.0]])
obsl = obs.toLinear()
assert obsl.dim() == 1, "toLinear does not generate an Orbit w/ dim=1 for FullOrbit"
assert numpy.all(
obsl.x() == obs.z()
), "Linear orbit generated w/ toLinear does not have the correct z"
assert numpy.all(
obsl.vx() == obs.vz()
), "Linear orbit generated w/ toLinear does not have the correct vx"
obs = Orbit([[1.0, 0.1, 1.1, 0.3], [1.0, -0.2, 1.3, -0.3]])
try:
obs.toLinear()
except AttributeError:
pass
else:
raise AttributeError(
"toLinear() applied to a planar Orbit did not raise an AttributeError"
)
# w/ scales
ro, vo = 10.0, 300.0
obs = Orbit(
[[1.0, 0.1, 1.1, 0.3, 0.0, 2.0], [1.0, -0.2, 1.3, -0.3, 0.0, 5.0]], ro=ro, vo=vo
)
obsl = obs.toLinear()
assert obsl.dim() == 1, "toLinwar does not generate an Orbit w/ dim=1 for FullOrbit"
assert numpy.all(
obsl.x() == obs.z()
), "Linear orbit generated w/ toLinear does not have the correct z"
assert numpy.all(
obsl.vx() == obs.vz()
), "Linear orbit generated w/ toLinear does not have the correct vx"
assert (
numpy.fabs(obs._ro - obsl._ro) < 10.0**-15.0
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
numpy.fabs(obs._vo - obsl._vo) < 10.0**-15.0
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obsl._zo is None
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obsl._solarmotion is None
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obs._roSet == obsl._roSet
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
assert (
obs._voSet == obsl._voSet
), "Linear orbit generated w/ toLinear does not have the proper physical scale and coordinate-transformation parameters associated with it"
return None
# Check that the routines that should return physical coordinates are turned off by turn_physical_off
def test_physical_output_off():
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0)
o = Orbit()
ro = o._ro
vo = o._vo
# turn off
o.turn_physical_off()
# Test positions
assert (
numpy.fabs(o.R() - o.R(use_physical=False)) < 10.0**-10.0
), "o.R() output for Orbit setup with ro= does not work as expected when turned off"
assert (
numpy.fabs(o.x() - o.x(use_physical=False)) < 10.0**-10.0
), "o.x() output for Orbit setup with ro= does not work as expected when turned off"
assert (
numpy.fabs(o.y() - o.y(use_physical=False)) < 10.0**-10.0
), "o.y() output for Orbit setup with ro= does not work as expected when turned off"
assert (
numpy.fabs(o.z() - o.z(use_physical=False)) < 10.0**-10.0
), "o.z() output for Orbit setup with ro= does not work as expected when turned off"
assert (
numpy.fabs(o.r() - o.r(use_physical=False)) < 10.0**-10.0
), "o.r() output for Orbit setup with ro= does not work as expected when turned off"
# Test velocities
assert (
numpy.fabs(o.vR() - o.vR(use_physical=False)) < 10.0**-10.0
), "o.vR() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.vT() - o.vT(use_physical=False)) < 10.0**-10.0
), "o.vT() output for Orbit setup with vo= does not work as expected"
assert (
numpy.fabs(o.vphi() - o.vphi(use_physical=False)) < 10.0**-10.0
), "o.vphi() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.vx() - o.vx(use_physical=False)) < 10.0**-10.0
), "o.vx() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.vy() - o.vy(use_physical=False)) < 10.0**-10.0
), "o.vy() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.vz() - o.vz(use_physical=False)) < 10.0**-10.0
), "o.vz() output for Orbit setup with vo= does not work as expected when turned off"
# Test energies
assert (
numpy.fabs(o.E(pot=lp) - o.E(pot=lp, use_physical=False)) < 10.0**-10.0
), "o.E() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.Jacobi(pot=lp) - o.Jacobi(pot=lp, use_physical=False))
< 10.0**-10.0
), "o.E() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.ER(pot=lp) - o.ER(pot=lp, use_physical=False)) < 10.0**-10.0
), "o.ER() output for Orbit setup with vo= does not work as expected when turned off"
assert (
numpy.fabs(o.Ez(pot=lp) - o.Ez(pot=lp, use_physical=False)) < 10.0**-10.0
), "o.Ez() output for Orbit setup with vo= does not work as expected when turned off"
# Test angular momentun
assert numpy.all(
numpy.fabs(o.L() - o.L(use_physical=False)) < 10.0**-10.0
), "o.L() output for Orbit setup with ro=,vo= does not work as expected when turned off"
# Test action-angle functions
assert (
numpy.fabs(
o.jr(pot=lp, type="staeckel", delta=0.5)
- o.jr(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.jr() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.jp(pot=lp, type="staeckel", delta=0.5)
- o.jp(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.jp() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.jz(pot=lp, type="staeckel", delta=0.5)
- o.jz(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.jz() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tr(pot=lp, type="staeckel", delta=0.5)
- o.Tr(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Tr() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tp(pot=lp, type="staeckel", delta=0.5)
- o.Tp(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Tp() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tz(pot=lp, type="staeckel", delta=0.5)
- o.Tz(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Tz() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Or(pot=lp, type="staeckel", delta=0.5)
- o.Or(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Or() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Op(pot=lp, type="staeckel", delta=0.5)
- o.Op(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Op() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Oz(pot=lp, type="staeckel", delta=0.5)
- o.Oz(pot=lp, type="staeckel", delta=0.5, use_physical=False)
)
< 10.0**-10.0
), "o.Oz() output for Orbit setup with ro=,vo= does not work as expected"
# Also test the times
assert (
numpy.fabs(o.time(1.0) - 1.0) < 10.0**-10.0
), "o.time() in physical coordinates does not work as expected when turned off"
assert (
numpy.fabs(o.time(1.0, ro=ro, vo=vo) - ro / vo / 1.0227121655399913)
< 10.0**-10.0
), "o.time() in physical coordinates does not work as expected when turned off"
return None
# Check that the routines that should return physical coordinates are turned
# back on by turn_physical_on
def test_physical_output_on():
from astropy import units
from galpy.orbit import Orbit
from galpy.potential import LogarithmicHaloPotential
lp = LogarithmicHaloPotential(normalize=1.0)
o = Orbit()
ro = o._ro
vo = o._vo
o_orig = o()
# turn off and on
o.turn_physical_off()
for ii in range(3):
if ii == 0:
o.turn_physical_on(ro=ro, vo=vo)
elif ii == 1:
o.turn_physical_on(ro=ro * units.kpc, vo=vo * units.km / units.s)
else:
o.turn_physical_on()
# Test positions
assert (
numpy.fabs(o.R() - o_orig.R(use_physical=True)) < 10.0**-10.0
), "o.R() output for Orbit setup with ro= does not work as expected when turned back on"
assert (
numpy.fabs(o.x() - o_orig.x(use_physical=True)) < 10.0**-10.0
), "o.x() output for Orbit setup with ro= does not work as expected when turned back on"
assert (
numpy.fabs(o.y() - o_orig.y(use_physical=True)) < 10.0**-10.0
), "o.y() output for Orbit setup with ro= does not work as expected when turned back on"
assert (
numpy.fabs(o.z() - o_orig.z(use_physical=True)) < 10.0**-10.0
), "o.z() output for Orbit setup with ro= does not work as expected when turned back on"
# Test velocities
assert (
numpy.fabs(o.vR() - o_orig.vR(use_physical=True)) < 10.0**-10.0
), "o.vR() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.vT() - o_orig.vT(use_physical=True)) < 10.0**-10.0
), "o.vT() output for Orbit setup with vo= does not work as expected"
assert (
numpy.fabs(o.vphi() - o_orig.vphi(use_physical=True)) < 10.0**-10.0
), "o.vphi() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.vx() - o_orig.vx(use_physical=True)) < 10.0**-10.0
), "o.vx() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.vy() - o_orig.vy(use_physical=True)) < 10.0**-10.0
), "o.vy() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.vz() - o_orig.vz(use_physical=True)) < 10.0**-10.0
), "o.vz() output for Orbit setup with vo= does not work as expected when turned back on"
# Test energies
assert (
numpy.fabs(o.E(pot=lp) - o_orig.E(pot=lp, use_physical=True)) < 10.0**-10.0
), "o.E() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.Jacobi(pot=lp) - o_orig.Jacobi(pot=lp, use_physical=True))
< 10.0**-10.0
), "o.E() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.ER(pot=lp) - o_orig.ER(pot=lp, use_physical=True))
< 10.0**-10.0
), "o.ER() output for Orbit setup with vo= does not work as expected when turned back on"
assert (
numpy.fabs(o.Ez(pot=lp) - o_orig.Ez(pot=lp, use_physical=True))
< 10.0**-10.0
), "o.Ez() output for Orbit setup with vo= does not work as expected when turned back on"
# Test angular momentun
assert numpy.all(
numpy.fabs(o.L() - o_orig.L(use_physical=True)) < 10.0**-10.0
), "o.L() output for Orbit setup with ro=,vo= does not work as expected when turned back on"
# Test action-angle functions
assert (
numpy.fabs(
o.jr(pot=lp, type="staeckel", delta=0.5)
- o_orig.jr(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.jr() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.jp(pot=lp, type="staeckel", delta=0.5)
- o_orig.jp(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.jp() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.jz(pot=lp, type="staeckel", delta=0.5)
- o_orig.jz(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.jz() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tr(pot=lp, type="staeckel", delta=0.5)
- o_orig.Tr(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Tr() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tp(pot=lp, type="staeckel", delta=0.5)
- o_orig.Tp(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Tp() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Tz(pot=lp, type="staeckel", delta=0.5)
- o_orig.Tz(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Tz() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Or(pot=lp, type="staeckel", delta=0.5)
- o_orig.Or(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Or() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Op(pot=lp, type="staeckel", delta=0.5)
- o_orig.Op(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Op() output for Orbit setup with ro=,vo= does not work as expected"
assert (
numpy.fabs(
o.Oz(pot=lp, type="staeckel", delta=0.5)
- o_orig.Oz(pot=lp, type="staeckel", delta=0.5, use_physical=True)
)
< 10.0**-10.0
), "o.Oz() output for Orbit setup with ro=,vo= does not work as expected"
# Also test the times
assert (
numpy.fabs(o.time(1.0) - o_orig.time(1.0, use_physical=True)) < 10.0**-10.0
), "o_orig.time() in physical coordinates does not work as expected when turned back on"
return None
# Test that Orbits can be pickled
def test_pickling():
import pickle
from galpy.orbit import Orbit
# Just test most common setup: 3D, 6 phase-D
vxvvs = [[1.0, 0.1, 1.0, 0.1, -0.2, 1.5], [0.1, 3.0, 1.1, -0.3, 0.4, 2.0]]
orbits = Orbit(vxvvs)
pickled = pickle.dumps(orbits)
orbits_unpickled = pickle.loads(pickled)
# Tests
assert (
orbits_unpickled.dim() == 3
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
orbits_unpickled.phasedim() == 6
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.R()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.R()[1] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vR()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vR()[1] - 3.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vT()[0] - 1.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vT()[1] - 1.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.z()[0] - 0.1) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.z()[1] + 0.3) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vz()[0] + 0.2) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.vz()[1] - 0.4) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.phi()[0] - 1.5) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
assert (
numpy.fabs(orbits_unpickled.phi()[1] - 2.0) < 1e-10
), "Orbits initialization with vxvv in 3D, 6 phase-D does not work as expected"
return None
def test_from_name_values():
from galpy.orbit import Orbit
# test Vega and Lacaille 8760
o = Orbit.from_name("Vega", "Lacaille 8760")
assert numpy.allclose(
o.ra(), [279.23473479, 319.31362024]
), "RA of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.dec(), [38.78368896, -38.86736390]
), "DEC of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.dist(), [1 / 130.23, 1 / 251.9124]
), "Parallax of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.pmra(), [200.94, -3258.996]
), "PMRA of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.pmdec(), [286.23, -1145.862]
), "PMDec of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.vlos(), [-13.50, 20.56]
), "radial velocity of Vega/Lacaille 8760 does not match SIMBAD value"
# test Vega and Lacaille 8760, as a list
o = Orbit.from_name(["Vega", "Lacaille 8760"])
assert numpy.allclose(
o.ra(), [279.23473479, 319.31362024]
), "RA of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.dec(), [38.78368896, -38.86736390]
), "DEC of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.dist(), [1 / 130.23, 1 / 251.9124]
), "Parallax of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.pmra(), [200.94, -3258.996]
), "PMRA of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.pmdec(), [286.23, -1145.862]
), "PMDec of Vega/Lacaille 8760 does not match SIMBAD value"
assert numpy.allclose(
o.vlos(), [-13.50, 20.56]
), "radial velocity of Vega/Lacaille 8760 does not match SIMBAD value"
return None
def test_from_name_name():
# Test that o.name gives the expected output
from galpy.orbit import Orbit
assert (
Orbit.from_name("LMC").name == "LMC"
), "Orbit.from_name does not appear to set the name attribute correctly"
assert numpy.char.equal(
Orbit.from_name(["LMC"]).name, numpy.char.array("LMC")
), "Orbit.from_name does not appear to set the name attribute correctly"
assert numpy.all(
numpy.char.equal(
Orbit.from_name(["LMC", "SMC"]).name, numpy.char.array(["LMC", "SMC"])
)
), "Orbit.from_name does not appear to set the name attribute correctly"
# Also slice
assert (
Orbit.from_name(["LMC", "SMC", "Fornax"])[-1].name == "Fornax"
), "Orbit.from_name does not appear to set the name attribute correctly"
assert numpy.all(
numpy.char.equal(
Orbit.from_name(["LMC", "SMC", "Fornax"])[:2].name,
numpy.char.array(["LMC", "SMC"]),
)
), "Orbit.from_name does not appear to set the name attribute correctly"
return None
|
jobovyREPO_NAMEgalpyPATH_START.@galpy_extracted@galpy-main@tests@test_orbits.py@.PATH_END.py
|
{
"filename": "spatial_dropout.py",
"repo_name": "keras-team/keras",
"repo_path": "keras_extracted/keras-master/keras/src/layers/regularization/spatial_dropout.py",
"type": "Python"
}
|
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.regularization.dropout import Dropout
class BaseSpatialDropout(Dropout):
def __init__(self, rate, seed=None, name=None, dtype=None):
super().__init__(rate, seed=seed, name=name, dtype=dtype)
def call(self, inputs, training=False):
if training and self.rate > 0:
return backend.random.dropout(
inputs,
self.rate,
noise_shape=self._get_noise_shape(inputs),
seed=self.seed_generator,
)
return inputs
def get_config(self):
return {
"rate": self.rate,
"seed": self.seed,
"name": self.name,
"dtype": self.dtype,
}
@keras_export("keras.layers.SpatialDropout1D")
class SpatialDropout1D(BaseSpatialDropout):
"""Spatial 1D version of Dropout.
This layer performs the same function as Dropout, however, it drops
entire 1D feature maps instead of individual elements. If adjacent frames
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, `SpatialDropout1D` will help promote independence
between feature maps and should be used instead.
Args:
rate: Float between 0 and 1. Fraction of the input units to drop.
Call arguments:
inputs: A 3D tensor.
training: Python boolean indicating whether the layer
should behave in training mode (applying dropout)
or in inference mode (pass-through).
Input shape:
3D tensor with shape: `(samples, timesteps, channels)`
Output shape: Same as input.
Reference:
- [Tompson et al., 2014](https://arxiv.org/abs/1411.4280)
"""
def __init__(self, rate, seed=None, name=None, dtype=None):
super().__init__(rate, seed=seed, name=name, dtype=dtype)
self.input_spec = InputSpec(ndim=3)
def _get_noise_shape(self, inputs):
input_shape = ops.shape(inputs)
return (input_shape[0], 1, input_shape[2])
@keras_export("keras.layers.SpatialDropout2D")
class SpatialDropout2D(BaseSpatialDropout):
"""Spatial 2D version of Dropout.
This version performs the same function as Dropout, however, it drops
entire 2D feature maps instead of individual elements. If adjacent pixels
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, `SpatialDropout2D` will help promote independence
between feature maps and should be used instead.
Args:
rate: Float between 0 and 1. Fraction of the input units to drop.
data_format: `"channels_first"` or `"channels_last"`.
In `"channels_first"` mode, the channels dimension (the depth)
is at index 1, in `"channels_last"` mode is it at index 3.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be `"channels_last"`.
Call arguments:
inputs: A 4D tensor.
training: Python boolean indicating whether the layer
should behave in training mode (applying dropout)
or in inference mode (pass-through).
Input shape:
4D tensor with shape: `(samples, channels, rows, cols)` if
data_format='channels_first'
or 4D tensor with shape: `(samples, rows, cols, channels)` if
data_format='channels_last'.
Output shape: Same as input.
Reference:
- [Tompson et al., 2014](https://arxiv.org/abs/1411.4280)
"""
def __init__(
self, rate, data_format=None, seed=None, name=None, dtype=None
):
super().__init__(rate, seed=seed, name=name, dtype=dtype)
self.data_format = backend.standardize_data_format(data_format)
self.input_spec = InputSpec(ndim=4)
def _get_noise_shape(self, inputs):
input_shape = ops.shape(inputs)
if self.data_format == "channels_first":
return (input_shape[0], input_shape[1], 1, 1)
elif self.data_format == "channels_last":
return (input_shape[0], 1, 1, input_shape[3])
def get_config(self):
base_config = super().get_config()
config = {
"data_format": self.data_format,
}
return {**base_config, **config}
@keras_export("keras.layers.SpatialDropout3D")
class SpatialDropout3D(BaseSpatialDropout):
"""Spatial 3D version of Dropout.
This version performs the same function as Dropout, however, it drops
entire 3D feature maps instead of individual elements. If adjacent voxels
within feature maps are strongly correlated (as is normally the case in
early convolution layers) then regular dropout will not regularize the
activations and will otherwise just result in an effective learning rate
decrease. In this case, SpatialDropout3D will help promote independence
between feature maps and should be used instead.
Args:
rate: Float between 0 and 1. Fraction of the input units to drop.
data_format: `"channels_first"` or `"channels_last"`.
In `"channels_first"` mode, the channels dimension (the depth)
is at index 1, in `"channels_last"` mode is it at index 4.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be `"channels_last"`.
Call arguments:
inputs: A 5D tensor.
training: Python boolean indicating whether the layer
should behave in training mode (applying dropout)
or in inference mode (pass-through).
Input shape:
5D tensor with shape: `(samples, channels, dim1, dim2, dim3)` if
data_format='channels_first'
or 5D tensor with shape: `(samples, dim1, dim2, dim3, channels)` if
data_format='channels_last'.
Output shape: Same as input.
Reference:
- [Tompson et al., 2014](https://arxiv.org/abs/1411.4280)
"""
def __init__(
self, rate, data_format=None, seed=None, name=None, dtype=None
):
super().__init__(rate, seed=seed, name=name, dtype=dtype)
self.data_format = backend.standardize_data_format(data_format)
self.input_spec = InputSpec(ndim=5)
def _get_noise_shape(self, inputs):
input_shape = ops.shape(inputs)
if self.data_format == "channels_first":
return (input_shape[0], input_shape[1], 1, 1, 1)
elif self.data_format == "channels_last":
return (input_shape[0], 1, 1, 1, input_shape[4])
def get_config(self):
base_config = super().get_config()
config = {
"data_format": self.data_format,
}
return {**base_config, **config}
|
keras-teamREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@src@layers@regularization@spatial_dropout.py@.PATH_END.py
|
{
"filename": "profile.py",
"repo_name": "scikit-image/scikit-image",
"repo_path": "scikit-image_extracted/scikit-image-main/skimage/measure/profile.py",
"type": "Python"
}
|
import numpy as np
from scipy import ndimage as ndi
from .._shared.utils import _validate_interpolation_order, _fix_ndimage_mode
def profile_line(
image,
src,
dst,
linewidth=1,
order=None,
mode='reflect',
cval=0.0,
*,
reduce_func=np.mean,
):
"""Return the intensity profile of an image measured along a scan line.
Parameters
----------
image : ndarray, shape (M, N[, C])
The image, either grayscale (2D array) or multichannel
(3D array, where the final axis contains the channel
information).
src : array_like, shape (2,)
The coordinates of the start point of the scan line.
dst : array_like, shape (2,)
The coordinates of the end point of the scan
line. The destination point is *included* in the profile, in
contrast to standard numpy indexing.
linewidth : int, optional
Width of the scan, perpendicular to the line
order : int in {0, 1, 2, 3, 4, 5}, optional
The order of the spline interpolation, default is 0 if
image.dtype is bool and 1 otherwise. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional
How to compute any values falling outside of the image.
cval : float, optional
If `mode` is 'constant', what constant value to use outside the image.
reduce_func : callable, optional
Function used to calculate the aggregation of pixel values
perpendicular to the profile_line direction when `linewidth` > 1.
If set to None the unreduced array will be returned.
Returns
-------
return_value : array
The intensity profile along the scan line. The length of the profile
is the ceil of the computed length of the scan line.
Examples
--------
>>> x = np.array([[1, 1, 1, 2, 2, 2]])
>>> img = np.vstack([np.zeros_like(x), x, x, x, np.zeros_like(x)])
>>> img
array([[0, 0, 0, 0, 0, 0],
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[0, 0, 0, 0, 0, 0]])
>>> profile_line(img, (2, 1), (2, 4))
array([1., 1., 2., 2.])
>>> profile_line(img, (1, 0), (1, 6), cval=4)
array([1., 1., 1., 2., 2., 2., 2.])
The destination point is included in the profile, in contrast to
standard numpy indexing.
For example:
>>> profile_line(img, (1, 0), (1, 6)) # The final point is out of bounds
array([1., 1., 1., 2., 2., 2., 2.])
>>> profile_line(img, (1, 0), (1, 5)) # This accesses the full first row
array([1., 1., 1., 2., 2., 2.])
For different reduce_func inputs:
>>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.mean)
array([0.66666667, 0.66666667, 0.66666667, 1.33333333])
>>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.max)
array([1, 1, 1, 2])
>>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.sum)
array([2, 2, 2, 4])
The unreduced array will be returned when `reduce_func` is None or when
`reduce_func` acts on each pixel value individually.
>>> profile_line(img, (1, 2), (4, 2), linewidth=3, order=0,
... reduce_func=None)
array([[1, 1, 2],
[1, 1, 2],
[1, 1, 2],
[0, 0, 0]])
>>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=np.sqrt)
array([[1. , 1. , 0. ],
[1. , 1. , 0. ],
[1. , 1. , 0. ],
[1.41421356, 1.41421356, 0. ]])
"""
order = _validate_interpolation_order(image.dtype, order)
mode = _fix_ndimage_mode(mode)
perp_lines = _line_profile_coordinates(src, dst, linewidth=linewidth)
if image.ndim == 3:
pixels = [
ndi.map_coordinates(
image[..., i],
perp_lines,
prefilter=order > 1,
order=order,
mode=mode,
cval=cval,
)
for i in range(image.shape[2])
]
pixels = np.transpose(np.asarray(pixels), (1, 2, 0))
else:
pixels = ndi.map_coordinates(
image, perp_lines, prefilter=order > 1, order=order, mode=mode, cval=cval
)
# The outputted array with reduce_func=None gives an array where the
# row values (axis=1) are flipped. Here, we make this consistent.
pixels = np.flip(pixels, axis=1)
if reduce_func is None:
intensities = pixels
else:
try:
intensities = reduce_func(pixels, axis=1)
except TypeError: # function doesn't allow axis kwarg
intensities = np.apply_along_axis(reduce_func, arr=pixels, axis=1)
return intensities
def _line_profile_coordinates(src, dst, linewidth=1):
"""Return the coordinates of the profile of an image along a scan line.
Parameters
----------
src : 2-tuple of numeric scalar (float or int)
The start point of the scan line.
dst : 2-tuple of numeric scalar (float or int)
The end point of the scan line.
linewidth : int, optional
Width of the scan, perpendicular to the line
Returns
-------
coords : array, shape (2, N, C), float
The coordinates of the profile along the scan line. The length of the
profile is the ceil of the computed length of the scan line.
Notes
-----
This is a utility method meant to be used internally by skimage functions.
The destination point is included in the profile, in contrast to
standard numpy indexing.
"""
src_row, src_col = src = np.asarray(src, dtype=float)
dst_row, dst_col = dst = np.asarray(dst, dtype=float)
d_row, d_col = dst - src
theta = np.arctan2(d_row, d_col)
length = int(np.ceil(np.hypot(d_row, d_col) + 1))
# we add one above because we include the last point in the profile
# (in contrast to standard numpy indexing)
line_col = np.linspace(src_col, dst_col, length)
line_row = np.linspace(src_row, dst_row, length)
# we subtract 1 from linewidth to change from pixel-counting
# (make this line 3 pixels wide) to point distances (the
# distance between pixel centers)
col_width = (linewidth - 1) * np.sin(-theta) / 2
row_width = (linewidth - 1) * np.cos(theta) / 2
perp_rows = np.stack(
[
np.linspace(row_i - row_width, row_i + row_width, linewidth)
for row_i in line_row
]
)
perp_cols = np.stack(
[
np.linspace(col_i - col_width, col_i + col_width, linewidth)
for col_i in line_col
]
)
return np.stack([perp_rows, perp_cols])
|
scikit-imageREPO_NAMEscikit-imagePATH_START.@scikit-image_extracted@scikit-image-main@skimage@measure@profile.py@.PATH_END.py
|
{
"filename": "ndio.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/nddata/mixins/ndio.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
# This module implements the I/O mixin to the NDData class.
from astropy.io import registry
__all__ = ["NDIOMixin"]
__doctest_skip__ = ["NDDataRead", "NDDataWrite"]
class NDDataRead(registry.UnifiedReadWrite):
"""Read and parse gridded N-dimensional data and return as an NDData-derived
object.
This function provides the NDDataBase interface to the astropy unified I/O
layer. This allows easily reading a file in the supported data formats,
for example::
>>> from astropy.nddata import CCDData
>>> dat = CCDData.read('image.fits')
Get help on the available readers for ``CCDData`` using the``help()`` method::
>>> CCDData.read.help() # Get help reading CCDData and list supported formats
>>> CCDData.read.help('fits') # Get detailed help on CCDData FITS reader
>>> CCDData.read.list_formats() # Print list of available formats
For more information see:
- https://docs.astropy.org/en/stable/nddata
- https://docs.astropy.org/en/stable/io/unified.html
Parameters
----------
*args : tuple, optional
Positional arguments passed through to data reader. If supplied the
first argument is the input filename.
format : str, optional
File format specifier.
cache : bool, optional
Caching behavior if file is a URL.
**kwargs : dict, optional
Keyword arguments passed through to data reader.
Returns
-------
out : `NDData` subclass
NDData-basd object corresponding to file contents
Notes
-----
"""
def __init__(self, instance, cls):
super().__init__(instance, cls, "read", registry=None)
# uses default global registry
def __call__(self, *args, **kwargs):
return self.registry.read(self._cls, *args, **kwargs)
class NDDataWrite(registry.UnifiedReadWrite):
"""Write this CCDData object out in the specified format.
This function provides the NDData interface to the astropy unified I/O
layer. This allows easily writing a file in many supported data formats
using syntax such as::
>>> from astropy.nddata import CCDData
>>> dat = CCDData(np.zeros((12, 12)), unit='adu') # 12x12 image of zeros
>>> dat.write('zeros.fits')
Get help on the available writers for ``CCDData`` using the``help()`` method::
>>> CCDData.write.help() # Get help writing CCDData and list supported formats
>>> CCDData.write.help('fits') # Get detailed help on CCDData FITS writer
>>> CCDData.write.list_formats() # Print list of available formats
For more information see:
- https://docs.astropy.org/en/stable/nddata
- https://docs.astropy.org/en/stable/io/unified.html
Parameters
----------
*args : tuple, optional
Positional arguments passed through to data writer. If supplied the
first argument is the output filename.
format : str, optional
File format specifier.
**kwargs : dict, optional
Keyword arguments passed through to data writer.
Notes
-----
"""
def __init__(self, instance, cls):
super().__init__(instance, cls, "write", registry=None)
# uses default global registry
def __call__(self, *args, **kwargs):
self.registry.write(self._instance, *args, **kwargs)
class NDIOMixin:
"""
Mixin class to connect NDData to the astropy input/output registry.
This mixin adds two methods to its subclasses, ``read`` and ``write``.
"""
read = registry.UnifiedReadWriteMethod(NDDataRead)
write = registry.UnifiedReadWriteMethod(NDDataWrite)
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@nddata@mixins@ndio.py@.PATH_END.py
|
{
"filename": "test_sed_bb.py",
"repo_name": "mirochaj/ares",
"repo_path": "ares_extracted/ares-main/examples/sources/test_sed_bb.py",
"type": "Python"
}
|
"""
test_sed_mcd.py
Author: Jordan Mirocha
Affiliation: University of Colorado at Boulder
Created on: Thu May 2 10:46:44 2013
Description: Plot a simple multi-color disk accretion spectrum.
"""
import ares
import numpy as np
import matplotlib.pyplot as pl
pars = \
{
'source_temperature': 1e4,
'source_Emin': 1.,
'source_Emax': 1e2,
'source_qdot': 1e50,
}
ls = [':', '--', '-']
for i, logT in enumerate([4, 4.5, 5]):
pars.update({'source_temperature': 10**logT})
src = ares.sources.Star(init_tabs=False, **pars)
bh = ares.analysis.Source(src)
ax = bh.PlotSpectrum(ls=ls[i],
label=r'$T_{{\ast}} = 10^{{{:.2g}}} \mathrm{{K}}$'.format(logT))
ax.plot([10.2]*2, [1e-8, 1], color='r', ls='--')
ax.plot([13.6]*2, [1e-8, 1], color='r', ls='--')
ax.legend(loc='lower left')
ax.set_ylim(1e-8, 1)
pl.draw()
|
mirochajREPO_NAMEaresPATH_START.@ares_extracted@ares-main@examples@sources@test_sed_bb.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/scipy/py3/scipy/sparse/linalg/_dsolve/setup.py",
"type": "Python"
}
|
from os.path import join, dirname
import sys
import glob
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info
from scipy._build_utils import numpy_nodepr_api
config = Configuration('_dsolve',parent_package,top_path)
config.add_data_dir('tests')
lapack_opt = get_info('lapack_opt',notfound_action=2)
if sys.platform == 'win32':
superlu_defs = [('NO_TIMER',1)]
else:
superlu_defs = []
superlu_defs.append(('USE_VENDOR_BLAS',1))
superlu_src = join(dirname(__file__), 'SuperLU', 'SRC')
sources = sorted(glob.glob(join(superlu_src, '*.c')))
headers = list(glob.glob(join(superlu_src, '*.h')))
config.add_library('superlu_src',
sources=sources,
macros=superlu_defs,
include_dirs=[superlu_src],
)
# Extension
ext_sources = ['_superlumodule.c',
'_superlu_utils.c',
'_superluobject.c']
config.add_extension('_superlu',
sources=ext_sources,
libraries=['superlu_src'],
depends=(sources + headers),
extra_info=lapack_opt,
**numpy_nodepr_api
)
# Add license files
config.add_data_files('SuperLU/License.txt')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@scipy@py3@scipy@sparse@linalg@_dsolve@setup.py@.PATH_END.py
|
{
"filename": "_style.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/candlestick/hoverlabel/font/_style.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class StyleValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="style", parent_name="candlestick.hoverlabel.font", **kwargs
):
super(StyleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "none"),
values=kwargs.pop("values", ["normal", "italic"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@candlestick@hoverlabel@font@_style.py@.PATH_END.py
|
{
"filename": "test_hitran_cdsd.py",
"repo_name": "radis/radis",
"repo_path": "radis_extracted/radis-master/radis/test/io/test_hitran_cdsd.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""Test parsers.
Notes
-----
Runs tests for radis/io so that they can be accessed by pytest (and hopefully
the CI test suite)
Examples
--------
Run all tests::
pytest (in command line, in project folder)
Run only fast tests (i.e: tests that have a 'fast' label)::
pytest -m fast
-------------------------------------------------------------------------------
"""
import os
from os.path import getmtime
from warnings import warn
import numpy as np
import pytest
from radis.api.cdsdapi import cdsd2df
from radis.api.hitranapi import hit2df
from radis.misc.warning import IrrelevantFileWarning
from radis.test.utils import getTestFile, setup_test_line_databases
@pytest.mark.fast
def test_hitran_names_match(verbose=True, warnings=True, *args, **kwargs):
"""Compare that HITRAN species defined in :mod:`radis.api.hitranapi` match
the nomenclature dictionary : :py:data:`radis.api.hitranapi.trans`.
This should be ensured by developers when adding new species.
"""
from radis.db.classes import (
HITRAN_CLASS1,
HITRAN_CLASS2,
HITRAN_CLASS3,
HITRAN_CLASS4,
HITRAN_CLASS5,
HITRAN_CLASS6,
HITRAN_CLASS7,
HITRAN_CLASS8,
HITRAN_CLASS9,
HITRAN_CLASS10,
HITRAN_MOLECULES,
)
from radis.misc.basics import compare_lists
all_hitran = (
HITRAN_CLASS1
+ HITRAN_CLASS2
+ HITRAN_CLASS3
+ HITRAN_CLASS4
+ HITRAN_CLASS5
+ HITRAN_CLASS6
+ HITRAN_CLASS7
+ HITRAN_CLASS8
+ HITRAN_CLASS9
+ HITRAN_CLASS10
)
all_hitran = list(set(all_hitran))
# All species in HITRAN groups should be valid HITRAN_MOLECULES names
for m in all_hitran:
if not m in HITRAN_MOLECULES:
raise ValueError(
"{0} is defined in HITRAN groups but has no HITRAN id".format(m)
)
# Species in 'HITRAN_MOLECULES' should be classified in groups, else nonequilibrium
# calculations are not possible.
if warnings and all_hitran != HITRAN_MOLECULES:
warn(
"Difference between HITRAN groups (left) and HITRAN id "
+ "dictionary (right). Some HITRAN species are not classified in "
+ "groups. Nonequilibrium calculations wont be possible for these!:\n"
+ "{0}".format(
compare_lists(
all_hitran, HITRAN_MOLECULES, verbose=False, return_string=True
)[1]
)
)
return
@pytest.mark.fast
def test_local_hitran_co(verbose=True, warnings=True, **kwargs):
"""Analyse some default files to make sure everything still works."""
# 1. Load
df = hit2df(getTestFile("hitran_CO_fragment.par"), cache="regen")
if verbose:
print("Read hitran_CO_fragment.par")
print("---------------------------")
print(df.head())
# 2. Test
assert list(df.loc[0, ["vu", "vl"]]) == [4, 4]
assert df.dtypes["vu"] == np.int64
assert df.dtypes["vl"] == np.int64
return True
def test_local_hitran_co2(verbose=True, warnings=True, **kwargs):
# 1. Load
df = hit2df(getTestFile("hitran_CO2_fragment.par"), cache="regen")
if verbose:
print("Read hitran_CO2_fragment.par")
print("----------------------------")
print(df.head())
# 2. Test
assert list(
df.loc[0, ["v1u", "v2u", "l2u", "v3u", "v1l", "v2l", "l2l", "v3l"]]
) == [4, 0, 0, 0, 0, 0, 0, 1]
assert df.dtypes["v1l"] == np.int64
assert df.dtypes["v3u"] == np.int64
return True
def test_local_hitran_h2o(verbose=True, warnings=True, **kwargs):
# 1. Load
df = hit2df(getTestFile("hitran_2016_H2O_2iso_2000_2100cm.par"), cache="regen")
if verbose:
print("Read hitran_2016_H2O_2iso_2000_2100cm.par")
print("-----------------------------------------")
print(df.head())
# 2. Test
assert list(df.loc[0, ["v1u", "v2u", "v3u", "v1l", "v2l", "v3l"]]) == [
0,
2,
0,
0,
1,
0,
]
assert df.loc[26, "ju"] == 5 # in .par : line 27, column 99-100
assert df.loc[27, "ju"] == 18 # in .par : line 28, column 99-100
assert df.dtypes["v1l"] == np.int64
assert df.dtypes["v3u"] == np.int64
assert df.dtypes["ju"] == np.int64
assert df.dtypes["Kau"] == np.int64
assert df.dtypes["Kcu"] == np.int64
assert df.dtypes["jl"] == np.int64
assert df.dtypes["Kal"] == np.int64
assert df.dtypes["Kcl"] == np.int64
return True
def test_local_hitemp_file(verbose=True, warnings=True, **kwargs):
"""Analyse some default files to make sure everything still works."""
# 1. Load
df = cdsd2df(
getTestFile("cdsd_hitemp_09_header.txt"), cache="regen", drop_non_numeric=True
)
if verbose:
print(df.head())
# 2. Tests
assert df.wav[3] == 2250.00096
# make sure P Q R is correctly replaced by drop_non_numeric:
assert "branch" in df
assert df["branch"].iloc[0] == 0 # Q
assert df["branch"].iloc[1] == 1 # R
return True
def test_irrelevant_file_loading(*args, **kwargs):
"""check that irrelevant files (irrelevant wavenumber) are not loaded"""
# For cdsd-hitemp files :
# Ensures file is not empty
df = cdsd2df(getTestFile("cdsd_hitemp_09_header.txt"), cache="regen")
assert len(df) > 0
# Now test nothing is loaded if asking for outside the wavenumber range
with pytest.raises(IrrelevantFileWarning):
df = cdsd2df(getTestFile("cdsd_hitemp_09_header.txt"), load_wavenum_min=100000)
with pytest.raises(IrrelevantFileWarning):
df = cdsd2df(getTestFile("cdsd_hitemp_09_header.txt"), load_wavenum_max=0.5)
# For HITRAN files :
# Ensures file is not empty
df = hit2df(getTestFile("hitran_2016_H2O_2iso_2000_2100cm.par"), cache="regen")
assert len(df) > 0
# Now test nothing is loaded if asking for outside the wavenumber range
with pytest.raises(IrrelevantFileWarning):
df = hit2df(
getTestFile("hitran_2016_H2O_2iso_2000_2100cm.par"), load_wavenum_min=100000
)
with pytest.raises(IrrelevantFileWarning):
df = hit2df(
getTestFile("hitran_2016_H2O_2iso_2000_2100cm.par"), load_wavenum_max=0.5
)
def _run_example(verbose=False):
from radis import SpectrumFactory
setup_test_line_databases(
verbose=verbose
) # add HITEMP-CO2-TEST in ~/radis.json if not there
sf = SpectrumFactory(
wavelength_min=4165,
wavelength_max=4200,
path_length=0.1,
pressure=20,
molecule="CO2",
isotope="1",
cutoff=1e-25, # cm/molecule
truncation=5, # cm-1
verbose=verbose,
)
sf.warnings["MissingSelfBroadeningWarning"] = "ignore"
sf.load_databank("HITRAN-CO2-TEST") # this database must be defined in ~/radis.json
def test_cache_regeneration(verbose=True, warnings=True, **kwargs):
"""Checks that if a line database is manually updated (last edited time changes),
its cache file will be regenerated automatically
"""
# Initialisation
# --------------
# Run case : we generate a first cache file
_run_example(verbose=verbose)
# get time when line database file was last modified
file_last_modification = getmtime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.par")
)
# get time when cache file was last modified
cache_last_modification = getmtime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.h5")
)
# To be sure, re run-example and make sure the cache file was not regenerated.
_run_example(verbose=verbose)
assert cache_last_modification == getmtime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.h5")
)
# Test
# ----
# Now we fake the manual editing of the database.
# change the time when line database was last modified :
stinfo = os.stat(getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.par"))
access_time = stinfo.st_atime
os.utime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.par"),
(file_last_modification + 1, access_time + 1),
)
file_last_modification_again = getmtime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.par")
)
assert file_last_modification_again > file_last_modification
# Run case : this should re-generated the cache file
_run_example(verbose=verbose)
cache_last_modification_again = getmtime(
getTestFile(r"hitran_co2_626_bandhead_4165_4200nm.h5")
)
assert cache_last_modification_again > cache_last_modification
def _run_testcases(verbose=True, *args, **kwargs):
test_hitran_names_match(verbose=verbose, *args, **kwargs)
test_local_hitran_co(verbose=verbose, *args, **kwargs)
test_local_hitran_co2(verbose=verbose, *args, **kwargs)
test_local_hitran_h2o(verbose=verbose, *args, **kwargs)
test_local_hitemp_file(verbose=verbose, *args, **kwargs)
test_irrelevant_file_loading()
test_cache_regeneration(verbose=verbose, *args, **kwargs)
return True
if __name__ == "__main__":
print("Testing test_hitran_cdsd.py: ", _run_testcases(verbose=True))
test_cache_regeneration(verbose=3)
|
radisREPO_NAMEradisPATH_START.@radis_extracted@radis-master@radis@test@io@test_hitran_cdsd.py@.PATH_END.py
|
{
"filename": "cube_minimal_example.py",
"repo_name": "pyspeckit/pyspeckit",
"repo_path": "pyspeckit_extracted/pyspeckit-master/examples/cube_minimal_example.py",
"type": "Python"
}
|
import pyspeckit
from pyspeckit.cubes.tests.test_cubetools import make_test_cube
import matplotlib.pylab as plt
import numpy as np
# generate a test spectral cube (10x10, with a 100 spectral channels)
make_test_cube((100,10,10), outfile='test.fits')
spc = pyspeckit.Cube('test.fits')
# do a crude noise estimate on the 30 edge channels
rmsmap = np.vstack([spc.cube[:15], spc.cube[85:]]).std(axis=0)
# get a cube of moments
spc.momenteach(vheight=False)
# fit each pixel taking its moment as an initial guess
spc.fiteach(fittype = 'gaussian',
guesses = spc.momentcube,
errmap = rmsmap,
signal_cut = 3, # ignore pixels with SNR<3
blank_value = np.nan,
start_from_point=(5,5))
spc.mapplot()
# show the fitted amplitude
spc.show_fit_param(0, cmap='viridis')
plt.show()
|
pyspeckitREPO_NAMEpyspeckitPATH_START.@pyspeckit_extracted@pyspeckit-master@examples@cube_minimal_example.py@.PATH_END.py
|
{
"filename": "run_mcmc_from_ses.py",
"repo_name": "3fon3fonov/exostriker",
"repo_path": "exostriker_extracted/exostriker-main/exostriker/lib/run_mcmc_from_ses.py",
"type": "Python"
}
|
#!/usr/bin/env python3
"""
@author: Trifon Trifonov
"""
import sys, os
sys.path.insert(0, './lib')
sys.path.append('./exostriker/lib/RV_mod/') #RV_mod directory must be in your path
#lib_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'lib')
#sys.path.insert(0,lib_path)
#os.chdir(os.path.dirname(os.path.abspath(__file__)))
import RV_mod as rv
import dill
arguments = len(sys.argv) - 1
fit=rv.signal_fit(name='mcmc_ses')
if arguments==3 and sys.argv[1] == '-ses' and os.path.exists(sys.argv[2]):
try:
file_pi = open(sys.argv[2], 'rb')
fit_ses = dill.load(file_pi)
file_pi.close()
fit = rv.check_for_missing_instances(fit,fit_ses)
except (ImportError, KeyError, AttributeError) as e:
print("%s cannot be recognaized"%sys.argv[2])
target_name = sys.argv[3]
#fit_ses.cwd = '/home/trifonov/git/exostriker_TIC149601126/exostriker'
fit = rv.run_mcmc(fit)
file_ses = open("%s_out.ses"%target_name, 'wb')
dill.dump(fit, file_ses)
file_ses.close()
|
3fon3fonovREPO_NAMEexostrikerPATH_START.@exostriker_extracted@exostriker-main@exostriker@lib@run_mcmc_from_ses.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattergl/unselected/textfont/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._color import ColorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._color.ColorValidator"]
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scattergl@unselected@textfont@__init__.py@.PATH_END.py
|
{
"filename": "_coloraxis.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattersmith/marker/_coloraxis.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColoraxisValidator(_plotly_utils.basevalidators.SubplotidValidator):
def __init__(
self, plotly_name="coloraxis", parent_name="scattersmith.marker", **kwargs
):
super(ColoraxisValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
dflt=kwargs.pop("dflt", None),
edit_type=kwargs.pop("edit_type", "calc"),
regex=kwargs.pop("regex", "/^coloraxis([2-9]|[1-9][0-9]+)?$/"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattersmith@marker@_coloraxis.py@.PATH_END.py
|
{
"filename": "test_pytrie.py",
"repo_name": "crossbario/crossbar",
"repo_path": "crossbar_extracted/crossbar-master/crossbar/router/test/test_pytrie.py",
"type": "Python"
}
|
#####################################################################################
#
# Copyright (c) typedef int GmbH
# SPDX-License-Identifier: EUPL-1.2
#
#####################################################################################
from twisted import trial
from pytrie import StringTrie
class TestPyTrie(trial.unittest.TestCase):
def test_empty_tree(self):
"""
Test trie ctor, and that is doesn't match on "any" prefix.
"""
t = StringTrie()
for key in ['', 'f', 'foo', 'foobar']:
with self.assertRaises(KeyError):
t.longest_prefix_value(key)
def test_contains(self):
"""
Test the contains operator.
"""
t = StringTrie()
test_keys = ['', 'f', 'foo', 'foobar', 'baz']
for key in test_keys:
t[key] = key
for key in test_keys:
self.assertTrue(key in t)
for key in ['x', 'fb', 'foob', 'fooba', 'bazz']:
self.assertFalse(key in t)
def test_longest_prefix_1(self):
"""
Test that keys are detected as prefix of themselfes.
"""
t = StringTrie()
test_keys = ['f', 'foo', 'foobar', 'baz']
for key in test_keys:
t[key] = key
for key in test_keys:
self.assertEqual(t.longest_prefix_value(key), key)
def test_longest_prefix_2(self):
"""
Test matching prefix lookups.
"""
t = StringTrie()
test_keys = ['f', 'foo', 'foobar']
for key in test_keys:
t[key] = key
test_keys = {
'foobarbaz': 'foobar',
'foobaz': 'foo',
'fool': 'foo',
'foo': 'foo',
'fob': 'f',
'fo': 'f',
'fx': 'f',
'f': 'f',
}
for key in test_keys:
self.assertEqual(t.longest_prefix_value(key), test_keys[key])
def test_longest_prefix_3(self):
"""
Test non-matching prefix lookups.
"""
t = StringTrie()
for key in ['x', 'fop', 'foobar']:
t[key] = key
for key in ['y', 'yfoo', 'fox', 'fooba']:
with self.assertRaises(KeyError):
t.longest_prefix_value(key)
# @unittest.skip("FIXME (broken unit test)")
# def test_longest_prefix_4(self):
# """
# Test that a trie with an empty string as a key contained
# matches a non-empty prefix matching lookup.
# """
# # stored_key = 'x' # this works (and of course it should!)
# stored_key = '' # this blows up! (and it _should_ work)
# test_key = 'xyz'
# t = StringTrie()
# t[stored_key] = stored_key
# self.assertTrue(stored_key in t)
# self.assertTrue(test_key.startswith(stored_key))
# self.assertEqual(t.longest_prefix_value(test_key), stored_key)
# # pytrie behavior is broken wrt to string keys of zero length!
# # See: https://bitbucket.org/gsakkis/pytrie/issues/4/string-keys-of-zero-length-are-not
# # We have a workaround in place for this at the relevant places.
# test_longest_prefix_4.skip = True
|
crossbarioREPO_NAMEcrossbarPATH_START.@crossbar_extracted@crossbar-master@crossbar@router@test@test_pytrie.py@.PATH_END.py
|
{
"filename": "ex_pareto_plot.py",
"repo_name": "statsmodels/statsmodels",
"repo_path": "statsmodels_extracted/statsmodels-main/statsmodels/examples/ex_pareto_plot.py",
"type": "Python"
}
|
"""
Created on Sun Aug 01 19:20:16 2010
Author: josef-pktd
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
nobs = 1000
r = stats.pareto.rvs(1, size=nobs)
#rhisto = np.histogram(r, bins=20)
rhisto, e = np.histogram(np.clip(r, 0 , 1000), bins=50)
plt.figure()
plt.loglog(e[:-1]+np.diff(e)/2, rhisto, '-o')
plt.figure()
plt.loglog(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o')
##plt.figure()
##plt.plot(e[:-1]+np.diff(e)/2, rhisto.cumsum(), '-o')
##plt.figure()
##plt.semilogx(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o')
rsind = np.argsort(r)
rs = r[rsind]
rsf = nobs-rsind.argsort()
plt.figure()
plt.loglog(rs, nobs-np.arange(nobs), '-o')
print(stats.linregress(np.log(rs), np.log(nobs-np.arange(nobs))))
plt.show()
|
statsmodelsREPO_NAMEstatsmodelsPATH_START.@statsmodels_extracted@statsmodels-main@statsmodels@examples@ex_pareto_plot.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "ParsonsRD/template_builder",
"repo_path": "template_builder_extracted/template_builder-master/README.md",
"type": "Markdown"
}
|
# ImPACT Template Builder
Set of classes to allow the production of image templates for use with the ImPACT Cherenkov telescope event reconstruction.
For more information about ImPACT see:
https://arxiv.org/abs/1403.2993
https://cta-observatory.github.io/ctapipe/reco/ImPACT.html
Classes are provided to perform the generation of CORSIKA input cards, sim_telarray configuration files and the final neural network fitting of the sim_telarray output. Templates are currently outputted in the ctapipe format, but functions for converting the the H.E.S.S. template format will be provided soon.
[](https://travis-ci.com/ParsonsRD/template_builder)
[](https://www.codacy.com/app/ParsonsRD/template_builder?utm_source=github.com&utm_medium=referral&utm_content=ParsonsRD/template_builder&utm_campaign=Badge_Grade)
[](https://codecov.io/gh/ParsonsRD/template_builder)
|
ParsonsRDREPO_NAMEtemplate_builderPATH_START.@template_builder_extracted@template_builder-master@README.md@.PATH_END.py
|
{
"filename": "mcTestCyclicBuffer.py",
"repo_name": "ACS-Community/ACS",
"repo_path": "ACS_extracted/ACS-master/LGPL/CommonSoftware/monitoring/moncollect/ws/test/mcTestCyclicBuffer.py",
"type": "Python"
}
|
#!/usr/bin/env python
#*******************************************************************************
# ALMA - Atacama Large Millimiter Array
# (c) Associated Universities Inc., 2002
# (c) European Southern Observatory, 2002
# Copyright by ESO (in the framework of the ALMA collaboration)
# and Cosylab 2002, All rights reserved
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
#
from Acspy.Clients.SimpleClient import PySimpleClient
from sys import argv
from sys import exit
from TMCDB import MonitorCollector
from TMCDB import propertySerailNumber
from omniORB import any
import MonitorErrImpl
import MonitorErr
import time
def get_id_comp_attr(blob):
return str((blob.propertyName, blob.propertySerialNumber))
# Check the following:
# - Arrays have arr_length values
# - All array values are equal
# - Blob data values are consecutive
# - There are at least min_blobs blobs
# - There are at most max_blobs blobs
# - When val0_gt is not None, the first blob must have a value greater than this value
# - When val0_lt is not None, the first blob must have a value lower than this value
# - When val0_eq is not None, the first blob must have a value equal to it
def check_data(blob, arr_length, val0_gt, val0_lt, val0_eq, min_blobs, max_blobs):
print "\t", blob.propertyName, blob.propertySerialNumber
last_values = []
values = []
for blobData in any.from_any(blob.blobDataSeq):
if type(blobData['value']) == list:
if len(blobData['value']) != arr_length:
print "Error! Expected array of %d values but was of size %d" % (arr_length, len(blobData['value']))
if len(blobData['value']) > 0:
value = blobData['value'][0]
all_equal = True
for val in blobData['value']:
if value != val:
all_equal = False
if not all_equal:
print "\t\tError! Not all values are equal: ", blobData['value']
else:
value = None
else:
value = blobData['value']
values.append(value)
if len(values) > 0:
consecutive = True
n = values[0]
if val0_gt != None:
if val0_gt >= n:
print "\t\tError! We expected first blob value was greater than: ", val0_gt, " but was ", n
else:
print "\t\tGreat! First blob is greater than: ", val0_gt
if val0_lt != None:
if val0_lt <= n:
print "\t\tError! We expected first blob value was lower than: ", val0_lt, " but was ", n
else:
print "\t\tGreat! First blob is lower than: ", val0_lt
if val0_eq != None:
if val0_eq != n:
print "\t\tError! We expected first blob value was: ", val0_eq, " but was ", n
else:
print "\t\tGreat! First blob is: ", val0_eq
for v in values:
if n != v:
print "\t\t", n, " != ", v
consecutive = False
n += 1
if not consecutive:
print "\t\tError! Values are not consecutive: ", values
else:
print "\t\tGreat! All values are consecutive"
if len(values) >= min_blobs:
print "\t\tGreat! Collected at least %d values: %d" % (min_blobs,len(values))
else:
print "\t\tError! Collected less than %d values: %d" % (min_blobs,len(values))
if len(values) <= max_blobs:
print "\t\tGreat! Collected at most %d values: %d" % (max_blobs,len(values))
else:
print "\t\tError! Collected more than %d values: %d" % (max_blobs,len(values))
return values[-1]
else:
print "\t\tNo values"
return None
# Make an instance of the PySimpleClient
simpleClient = PySimpleClient()
mc = simpleClient.getComponent(argv[1])
# First test: Nominal test. Some data buffered and recovered
cname = 'MC_TEST_COMPONENT'
try:
tc = simpleClient.getComponent(cname)
psns =[propertySerailNumber('doubleSeqProp', ['12124']),propertySerailNumber('doubleProp', ['3432535'])]
mc.registerMonitoredDeviceWithMultipleSerial(cname, psns)
tc.reset();
mc.startMonitoring(cname)
time.sleep(2)
mc.stopMonitoring(cname)
except MonitorErr.RegisteringDeviceProblemEx, _ex:
ex = MonitorErrImpl.RegisteringDeviceProblemExImpl(exception=_ex)
ex.Print();
time.sleep(1)
data = mc.getMonitorData()
# First log entry: Print results of the first part of the test
last_values = {}
print "Nominal test. Number of Devices:", len(data);
for d in data:
print d.componentName, d.deviceSerialNumber
for blob in d.monitorBlobs:
id_comp_attr = get_id_comp_attr(blob)
last_values[id_comp_attr] = check_data(blob, 25, None, None, None, 2, 2)
"""
id_comp = get_id_comp_attr(blob)
print "\t", blob.propertyName, blob.propertySerialNumber
for blobData in any.from_any(blob.blobDataSeq):
print "\t\t", blobData
"""
# Second test: Cyclic buffering activation. Reseting MC_TEST_COMPONENT at beginning, verification of data loss
try:
tc.reset();
mc.startMonitoring(cname)
time.sleep(220)
mc.stopMonitoring(cname)
except MonitorErr.RegisteringDeviceProblemEx, _ex:
ex = MonitorErrImpl.RegisteringDeviceProblemExImpl(exception=_ex)
ex.Print();
time.sleep(1)
data = mc.getMonitorData()
# Second log entry: Print results of the second part of the test
print "Cyclic buffer test. Number of Devices:", len(data);
for d in data:
print d.componentName, d.deviceSerialNumber
for blob in d.monitorBlobs:
id_comp_attr = get_id_comp_attr(blob)
lv = last_values[id_comp_attr]
last_values[id_comp_attr] = check_data(blob, 25, lv + 15, None, None, 200, 200)
"""
print "\t", blob.propertyName, blob.propertySerialNumber
for blobData in any.from_any(blob.blobDataSeq):
print "\t\t", blobData
"""
# Third test: Verification of cyclic buffer restarted after acquisition (no MC_TEST_COMPONENT reset)
try:
mc.startMonitoring(cname)
time.sleep(10)
mc.stopMonitoring(cname)
except MonitorErr.RegisteringDeviceProblemEx, _ex:
ex = MonitorErrImpl.RegisteringDeviceProblemExImpl(exception=_ex)
ex.Print();
data = mc.getMonitorData()
# Third log entry: Print results of the third part of the test
print "Cyclic buffer reset test. Number of Devices:", len(data);
for d in data:
print d.componentName, d.deviceSerialNumber
for blob in d.monitorBlobs:
id_comp_attr = get_id_comp_attr(blob)
lv = last_values[id_comp_attr]
last_values[id_comp_attr] = check_data(blob, 25, lv, lv + 3, None, 8, 13)
"""
print "\t", blob.propertyName, blob.propertySerialNumber
i=0
for blobData in any.from_any(blob.blobDataSeq):
if i<5:
print "\t\t", blobData
i+=1
"""
mc.deregisterMonitoredDevice(cname)
#cleanly disconnect
simpleClient.releaseComponent(argv[1])
simpleClient.disconnect()
|
ACS-CommunityREPO_NAMEACSPATH_START.@ACS_extracted@ACS-master@LGPL@CommonSoftware@monitoring@moncollect@ws@test@mcTestCyclicBuffer.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "ggalloni/cobaya",
"repo_path": "cobaya_extracted/cobaya-master/cobaya/likelihoods/planck_2018_highl_CamSpec/__init__.py",
"type": "Python"
}
|
ggalloniREPO_NAMEcobayaPATH_START.@cobaya_extracted@cobaya-master@cobaya@likelihoods@planck_2018_highl_CamSpec@__init__.py@.PATH_END.py
|
|
{
"filename": "plot.py",
"repo_name": "deepsphere/deepsphere-cosmo-tf1",
"repo_path": "deepsphere-cosmo-tf1_extracted/deepsphere-cosmo-tf1-master/deepsphere/plot.py",
"type": "Python"
}
|
"""Plotting module."""
from __future__ import division
from builtins import range
import datetime
import numpy as np
import healpy as hp
import matplotlib as mpl
import matplotlib.pyplot as plt
from . import utils
def plot_filters_gnomonic(filters, order=10, ind=0, title='Filter {}->{}', graticule=False):
"""Plot all filters in a filterbank in Gnomonic projection."""
nside = hp.npix2nside(filters.G.N)
reso = hp.pixelfunc.nside2resol(nside=nside, arcmin=True) * order / 100
rot = hp.pix2ang(nside=nside, ipix=ind, nest=True, lonlat=True)
maps = filters.localize(ind, order=order)
nrows, ncols = filters.n_features_in, filters.n_features_out
if maps.shape[0] == filters.G.N:
# FIXME: old signal shape when not using Chebyshev filters.
shape = (nrows, ncols, filters.G.N)
maps = maps.T.reshape(shape)
else:
if nrows == 1:
maps = np.expand_dims(maps, 0)
if ncols == 1:
maps = np.expand_dims(maps, 1)
# Plot everything.
# fig, axes = plt.subplots(nrows, ncols, figsize=(17, 17/ncols*nrows),
# squeeze=False, sharex='col', sharey='row')
cm = plt.cm.seismic
cm.set_under('w')
a = max(abs(maps.min()), maps.max())
ymin, ymax = -a,a
for row in range(nrows):
for col in range(ncols):
map = maps[row, col, :]
hp.gnomview(map.flatten(), nest=True, rot=rot, reso=reso, sub=(nrows, ncols, col+row*ncols+1),
title=title.format(row, col), notext=True, min=ymin, max=ymax, cbar=False, cmap=cm,
margins=[0.003,0.003,0.003,0.003],)
# if row == nrows - 1:
# #axes[row, col].xaxis.set_ticks_position('top')
# #axes[row, col].invert_yaxis()
# axes[row, col].set_xlabel('out map {}'.format(col))
# if col == 0:
# axes[row, col].set_ylabel('in map {}'.format(row))
# fig.suptitle('Gnomoinc view of the {} filters in the filterbank'.format(filters.n_filters))#, y=0.90)
# return fig
if graticule:
with utils.HiddenPrints():
hp.graticule(verbose=False)
def plot_filters_section(filters,
order=10,
xlabel='out map {}',
ylabel='in map {}',
title='Sections of the {} filters in the filterbank',
figsize=None,
**kwargs):
"""Plot the sections of all filters in a filterbank."""
nside = hp.npix2nside(filters.G.N)
npix = hp.nside2npix(nside)
# Create an inverse mapping from nest to ring.
index = hp.reorder(range(npix), n2r=True)
# Get the index of the equator.
index_equator, ind = get_index_equator(nside, order)
nrows, ncols = filters.n_features_in, filters.n_features_out
maps = filters.localize(ind, order=order)
if maps.shape[0] == filters.G.N:
# FIXME: old signal shape when not using Chebyshev filters.
shape = (nrows, ncols, filters.G.N)
maps = maps.T.reshape(shape)
else:
if nrows == 1:
maps = np.expand_dims(maps, 0)
if ncols == 1:
maps = np.expand_dims(maps, 1)
# Make the x axis: angular position of the nodes in degree.
angle = hp.pix2ang(nside, index_equator, nest=True)[1]
angle -= abs(angle[-1] + angle[0]) / 2
angle = angle / (2 * np.pi) * 360
if figsize==None:
figsize = (12, 12/ncols*nrows)
print(ncols, nrows)
# Plot everything.
fig, axes = plt.subplots(nrows, ncols, figsize=figsize,
squeeze=False, sharex='col', sharey='row')
ymin, ymax = 1.05*maps.min(), 1.05*maps.max()
for row in range(nrows):
for col in range(ncols):
map = maps[row, col, index_equator]
axes[row, col].plot(angle, map, **kwargs)
axes[row, col].set_ylim(ymin, ymax)
if row == nrows - 1:
#axes[row, col].xaxis.set_ticks_position('top')
#axes[row, col].invert_yaxis()
axes[row, col].set_xlabel(xlabel.format(col))
if col == 0:
axes[row, col].set_ylabel(ylabel.format(row))
fig.suptitle(title.format(filters.n_filters))#, y=0.90)
return fig
def plot_index_filters_section(filters, order=10, rot=(180,0,180)):
"""Plot the indexes used for the function `plot_filters_section`"""
nside = hp.npix2nside(filters.G.N)
npix = hp.nside2npix(nside)
index_equator, center = get_index_equator(nside, order)
sig = np.zeros([npix])
sig[index_equator] = 1
sig[center] = 2
hp.mollview(sig, nest=True, title='', cbar=False, rot=rot)
def get_index_equator(nside, radius):
"""Return some indexes on the equator and the center of the index."""
npix = hp.nside2npix(nside)
# Create an inverse mapping from nest to ring.
index = hp.reorder(range(npix), n2r=True)
# Center index
center = index[npix // 2]
# Get the value on the equator back.
equator_part = range(npix//2-radius, npix//2+radius+1)
index_equator = index[equator_part]
return index_equator, center
def plot_with_std(x, y=None, color=None, alpha=0.2, ax=None, **kwargs):
if y is None:
y = x
x = np.arange(y.shape[1])
ystd = np.std(y, axis=0)
ymean = np.mean(y, axis=0)
if ax is None:
ax = plt.gca()
lines = ax.plot(x, ymean, color=color, **kwargs)
color = lines[0].get_color()
ax.fill_between(x, ymean - ystd, ymean + ystd, alpha=alpha, color=color)
return ax
def zoom_mollview(sig, cmin=None, cmax=None, nest=True):
from numpy.ma import masked_array
from matplotlib.patches import Rectangle
if cmin is None:
cmin = np.min(sig)
if cmax is None:
cmax = np.max(sig)
projected = hp.mollview(sig, return_projected_map=True, nest=nest)
plt.clf()
nmesh = 400
loleft = -35
loright = -30
grid = hp.cartview(sig, latra=[-2.5,2.5], lonra=[loleft,loright], fig=1, xsize=nmesh, return_projected_map=True, nest=nest)
plt.clf()
nside = hp.npix2nside(len(sig))
theta, phi = hp.pix2ang(nside, np.arange(hp.nside2npix(nside)))
# Get position for the zoom window
theta_min = 87.5/180*np.pi
theta_max = 92.5/180*np.pi
delta_theta = 0.55/180*np.pi
phi_min = (180 - loleft)/180.0*np.pi
phi_max = (180 - loright)/180.0*np.pi
delta_phi = 0.55/180*np.pi
angles = np.array([theta, phi]).T
m0 = np.argmin(np.sum((angles - np.array([theta_max, phi_max]))**2, axis=1))
m1 = np.argmin(np.sum((angles - np.array([theta_max, phi_min]))**2, axis=1))
m2 = np.argmin(np.sum((angles - np.array([theta_min, phi_max]))**2, axis=1))
m3 = np.argmin(np.sum((angles - np.array([theta_min, phi_min]))**2, axis=1))
proj = hp.projector.MollweideProj(xsize=800)
m0 = proj.xy2ij(proj.vec2xy(hp.pix2vec(ipix=m0, nside=nside)))
m1 = proj.xy2ij(proj.vec2xy(hp.pix2vec(ipix=m1, nside=nside)))
m2 = proj.xy2ij(proj.vec2xy(hp.pix2vec(ipix=m2, nside=nside)))
m3 = proj.xy2ij(proj.vec2xy(hp.pix2vec(ipix=m3, nside=nside)))
width = m0[1] - m1[1]
height = m2[0] - m1[0]
test_pro = np.full(shape=(400, 1400), fill_value=-np.inf)
test_pro_1 = np.full(shape=(400, 1400), fill_value=-np.inf)
test_pro[:,:800] = projected
test_pro_1[:,1000:1400] = grid.data
tt_0 = masked_array(test_pro, test_pro<-1000)
tt_1 = masked_array(test_pro_1, test_pro_1<-1000)
fig = plt.figure(frameon=False, figsize=(12,8))
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
ax = fig.gca()
plt.plot(np.linspace(m1[1]+width, 1000), np.linspace(m1[0], 0), 'k-')
plt.plot(np.linspace(m1[1]+width, 1000), np.linspace(m2[0], 400), 'k-')
plt.vlines(x=[1000, 1399], ymin=0, ymax=400)
plt.hlines(y=[0,399], xmin=1000, xmax=1400)
c = Rectangle((m1[1], m1[0]), width, height, color='k', fill=False, linewidth=3, zorder=100)
ax.add_artist(c)
cm = plt.cm.Blues
cm = plt.cm.RdBu_r
# cm = plt.cm.coolwarm # Not working, I do not know why it is not working
# cm.set_bad("white")
im1 = ax.imshow(tt_0, cmap=cm, vmin=cmin, vmax=cmax)
cbaxes1 = fig.add_axes([0.08,0.2,0.4,0.04])
cbar1 = plt.colorbar(im1, orientation="horizontal", cax=cbaxes1)
im2 = ax.imshow(tt_1, cmap=cm, vmin=cmin, vmax=cmax)
cbaxes2 = fig.add_axes([1.02,0.285,0.025,0.43])
cbar2 = plt.colorbar(im2, orientation="vertical", cax=cbaxes2)
plt.xticks([])
plt.yticks([])
return fig
def plot_loss(loss_training, loss_validation, t_step, eval_frequency):
x_step = np.arange(len(loss_training)) * eval_frequency
x_time = t_step * x_step
x_time = [datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=sec) for sec in x_time]
fig, ax_step = plt.subplots()
ax_step.semilogy(x_step, loss_training, '.-', label='training')
ax_step.semilogy(x_step, loss_validation, '.-', label='validation')
ax_time = ax_step.twiny()
ax_time.semilogy(x_time, loss_training, linewidth=0)
fmt = mpl.dates.DateFormatter('%H:%M:%S')
ax_time.xaxis.set_major_formatter(fmt)
ax_step.set_xlabel('Training step')
ax_time.set_xlabel('Training time [s]')
ax_step.set_ylabel('Loss')
ax_step.grid(which='both')
ax_step.legend()
|
deepsphereREPO_NAMEdeepsphere-cosmo-tf1PATH_START.@deepsphere-cosmo-tf1_extracted@deepsphere-cosmo-tf1-master@deepsphere@plot.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "JohannesBuchner/BXA",
"repo_path": "BXA_extracted/BXA-master/bxa/sherpa/__init__.py",
"type": "Python"
}
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
BXA (Bayesian X-ray Analysis) for Sherpa
Copyright: Johannes Buchner (C) 2013-2019
"""
from __future__ import print_function
import os
from math import log10, isnan, isinf
if 'MAKESPHINXDOC' not in os.environ:
import sherpa.astro.ui as ui
from sherpa.stats import Cash, CStat
import numpy
from .priors import *
from .galabs import auto_galactic_absorption
from .solver import BXASolver, default_logging
def nested_run(
id=None, otherids=(), prior=None, parameters=None,
sampling_efficiency=0.3, evidence_tolerance=0.5,
n_live_points=400, outputfiles_basename='chains/', **kwargs
):
"""
deprecated, use BXASolver instead.
"""
solver = BXASolver(
id=id, otherids=otherids, prior=prior, parameters=parameters,
outputfiles_basename=outputfiles_basename)
return solver.run(
evidence_tolerance=evidence_tolerance,
n_live_points=n_live_points, **kwargs)
|
JohannesBuchnerREPO_NAMEBXAPATH_START.@BXA_extracted@BXA-master@bxa@sherpa@__init__.py@.PATH_END.py
|
{
"filename": "cubicSpline.py",
"repo_name": "mzechmeister/serval",
"repo_path": "serval_extracted/serval-master/src/cubicSpline.py",
"type": "Python"
}
|
from __future__ import print_function
# module cubicSpline
''' k = curvatures(xData,yData).
Returns the curvatures of cubic spline at its knots.
y = evalSpline(xData,yData,k,x).
Evaluates cubic spline at x. The curvatures k can be
computed with the function 'curvatures'.
From:
http://www.dur.ac.uk/physics.astrolab/py_source/kiusalaas/v1_with_numpy/cubicSpline.py
Example
from numpy import *
import cubicSpline
x=arange(10)
y=sin(x)
xx=arange(9)+0.5
k=cubicSpline.curvatures(x,y)
yy=cubicSpline.evalSpline(x,y,k,xx)
yy
'''
import numpy as np
import LUdecomp3
from numpy import logical_and, asarray,zeros_like,floor,append
from numpy.core.umath import sqrt, exp, greater, less, cos, add, sin, \
less_equal, greater_equal
import spl_int
def spl_c(x,y):
'''
spline curvature coefficients
input: x,y data knots
returns: tuple (datax, datay, curvature coeffients at knots)
'''
n = np.size(x)
if np.size(y)==n:
return spl_int.spl_int(x,y,n)
def spl_ev(xx,xyk):
'''
evalutes spline at xx
'''
#import pdb; pdb.set_trace()
#x,y,k=xyk
#return spl_int.spl_ev(x,y,k,np.size(x),xx,np.size(xx))
return spl_int.spl_ev(xyk[0],xyk[1],xyk[2],np.size(xyk[0]),xx,np.size(xx))
def spl_cf(x,y):
'''Fast creation of a cubic Spline.
Endpoints.
Returns
-------
xyk : tuple with knots and knot derivatives y, y', y'', y
'''
n = np.size(x)
if np.size(y)==n:
return spl_int.spl_intf(x, y, n)
def spl_evf(xx, xyk, der=0):
"""
der : derivative
Example
-------
>>> x = np.arange(9)
>>> y = x**2
>>> xyk = spl_cf(x, y)
>>> spl_evf(x, xyk)
Notes
-----
y(x) = a + bx + cx**2 + dx**3
y'(x) = b + 2cx + 3dx**2
y''(x) = 2c + 6dx
y'''(x) = 6d
Endpoint:
y_n(1) = a + b + c + d = y_n
b_n(1) = b + 2c + 3d
k_n(1) = 0
d(1) = 6d
"""
xx = np.array(xx)
x, a, b, k, d = xyk
b_n = b[-1] + k[-2] + 3*d[-1] # dummy endpoints
d_n = 0
if der==0:
# y(x) = a + bx + cx**2 + dx**3
pass
elif der==1:
# y'(x) = b + 2cx + 3dx**2
a, b, k, d = np.append(b, b_n), k[:-1], 3*np.append(d,d_n), 0*d
elif der==2:
# y''(x) = 2c + 6dx = k + 6dx
# c = k/2
a, b, k, d = k, 6*d, 0*k, 0*d
elif der==3:
a, b, k, d = 6*np.append(d, d_n), 0*b, 0*k, 0*d
else:
raise Exception('Derivative %s not implemented.'%der)
return spl_int.spl_evf(x, a, b, k, d, np.size(xyk[0]), xx, np.size(xx))
def spl_eq_c(x,y):
'''
spline curvature coefficients
input: x,y data knots
returns: tuple (datax, datay, curvature coeffients at knots)
'''
n = np.size(x)
if np.size(y)==n:
return spl_int.spl_eq_int(x,y,n)
def spl_eq_ev(xx,xyk):
'''
evalutes spline at xx
xyk tuple with data knots and curvature from spl_c
'''
return spl_int.spl_eq_ev(xyk[0],xyk[1],xyk[2],np.size(xyk[0]),xx,np.size(xx))
def curva(x,y):
# faster
# Compute the hi and bi
# h,b = ( x[i+1]-xi,y[i+1]-y[i] for i,xi in enumerate(x[:-1]))
x = x[1:] - x[:-1] # =h =dx
y = (y[1:] - y[:-1]) / x # =b =dy
# Gaussian Elimination
#u[1:],v[1:] = (u for a,b in zip(u,v) )
u = 2*(x[:-1] + x[1:]) # l #c
v = 6*(y[1:] - y[:-1]) # alpha #d
#for i,h in enumerate(x[1:-1]):
#u[i+1] -= h**2/u[i]
#v[i+1] -= h*v[i]/u[i]
u[1:] -= x[1:-1]**2/u[:-1]
v[1:] -= x[1:-1]*v[:-1]/u[:-1]
# Back-substitution
y[:-1] = v/u
y[-1] = 0
#for i in range(len(y[1:])):
#y[-i-2] -= (x[-i-1]*y[-i-1]/u[-i-1])
y[-2::-1] = y[-2::-1]-(x[:0:-1]*y[:0:-1]/u[::-1])
#print y[1:0]
return append(0,y)
def curva_slow(x,y):
# Compute t,he hi and bi
# h,b = ( x[i+1]-xi,y[i+1]-y[i] for i,xi in enumerate(x[:-1]))
x = x[1:] - x[:-1] # =h =dx
y = (y[1:] - y[:-1]) / x # =b =dy
# Gaussian Elimination
#u[1:],v[1:] = (u for a,b in zip(u,v) )
#x = 2*(x[:-1] + x[1:]) # u = 2*(h[:-1] + h[1:]) = 2* dh =2 ddx
#y = 6*(y[:-1] - y[1:]) # v = 6*(b[:-1] - b[1:]) ~ ddy
#u[1]=2*(x[0]+x[1])
#v[1]=2*(y[1]-y[0])
u = 2*(x[:-1] + x[1:]) # l #c
v = 6*(y[1:] - y[:-1]) # alpha #d
for i,h in enumerate(x[1:-1]):
# if i==0: print 'slo_here'
u[i+1] = u[i+1] - h**2/u[i]
v[i+1] -= h*v[i]/u[i]
# Back-substitution
y[:-1] = v/u
y[-1] = 0
for i in range(len(y[1:])):
y[-i-2] -= (x[-i-1]*y[-i-1]/u[-i-1])
return append(0,y)
def curvatures(xData,yData):
n = len(xData) - 1
c = np.zeros((n),dtype=float)
d = np.ones((n+1),dtype=float)
e = np.zeros((n),dtype=float)
k = np.zeros((n+1),dtype=float)
c[0:n-1] = xData[0:n-1] - xData[1:n]
d[1:n] = 2.0*(xData[0:n-1] - xData[2:n+1])
e[1:n] = xData[1:n] - xData[2:n+1]
k[1:n] = 6.0*(yData[0:n-1] - yData[1:n]) /c[0:n-1] \
-6.0*(yData[1:n] - yData[2:n+1]) /e[1:n]
LUdecomp3.LUdecomp3(c,d,e)
LUdecomp3.LUsolve3(c,d,e,k)
return k
def curvatures_org(xData,yData):
n = len(xData) - 1
c = np.zeros((n),dtype=float)
d = np.ones((n+1),dtype=float)
e = np.zeros((n),dtype=float)
k = np.zeros((n+1),dtype=float)
c[0:n-1] = xData[0:n-1] - xData[1:n]
d[1:n] = 2.0*(xData[0:n-1] - xData[2:n+1])
e[1:n] = xData[1:n] - xData[2:n+1]
k[1:n] =6.0*(yData[0:n-1] - yData[1:n]) \
/(xData[0:n-1] - xData[1:n]) \
-6.0*(yData[1:n] - yData[2:n+1]) \
/(xData[1:n] - xData[2:n+1])
LUdecomp3.LUdecomp3(c,d,e)
LUdecomp3.LUsolve3(c,d,e,k)
return k
#def evalSpline_new(xData,yData,k,xx):
#global m,iLeft,iRight
#iLeft = 0
#iRight = len(xData)- 1
#m=-1
#xn=xData[1:]
#d=xn-xData[:-1]
#def do(x):
#global m,iLeft,iRight
#m+=1
#h = d[i]
#A = (x - xn[i])/h
#B = (x - xData[i])/h
#return ((A**3 - A)*k[i] - (B**3 - B)*k[i+1])/6.0*h*h \
#+ (yData[i]*A - yData[i+1]*B)
#for x in xx;while x >= xData[iLeft] and iLeft<iRight: iLeft += 1
#i=iLeft-1]
#return map( lambda x: do(x), xx)
def evalSpline_for(xData,yData,k,xx):
# very slow
h = xData[1]-xData[0]
n=np.arange(len(xData)-1)
for i,x in enumerate(xx):
a = (x - (i+1))/h
b = a+1
#print i
xx[i]=((a-a**3)*k[i] - (b-b**3)*k[i+1])/6.0*h*h \
- (yData[i]*a - yData[i+1]*b)
return xx
def evalSpline_vec(xData,yData,k,xx):
h = xData[1]-xData[0]
n=np.arange(len(xx))
AA = (xx - (n+1))/h
BB = AA+1
return ((AA-AA**3)*k[:-1] - (BB-BB**3)*k[1:])/6.0*h*h \
- (yData[:-1]*AA - yData[1:]*BB)
def evalSpline_gen(xData,yData,k,xx):
# generator expression
h = xData[1]-xData[0]
n=np.arange(len(xx))
AA = (xx - (n+1))/h
BB = AA+1
return (((AA[i]-AA[i]**3)*k[i] - (BB[i]-BB[i]**3)*k[i+1])*h*h/6.0 \
- (yData[i]*AA[i] - yData[i+1]*BB[i]) for i in np.arange(len(xx)))
def evalSpline(xData,yData,k,xx):
# generator expression
# S(x)=a_i+b_i(x-x_i)+c_i(x-x_i)^2+d_i(x-x_i)^2
# a_i=y_i
# b_i=-h_i/6*z_(i+1)-h_i/3*z_i+ (y_(i+1)-y_i)/h_i
# c_i=z_i/2
# d_i= (z_(i+1)-z_i)/6/h_i
# x=x-x_i=x-i = > S=a+x*(b+x*(c+x*d))
h = xData[1]-xData[0]
n=np.arange(len(xData)-1)
AA = (xx - (n+1))/h
BB = AA+1
return ( yData[i]+x*( -k[i+1]/6. - k[i]/3 + (yData[i+1]-yData[i]) + x*(k[i]/2 +x *(k[i+1]-k[i])/6)) for i,x in enumerate(xx-np.arange(len(xx))) )
#def evalSpline(xData,yData,k,xx):
## generator expression
#h = xData[1]-xData[0]
#n=np.arange(len(xData)-1)
#AA = (xx - (n+1))/h
#BB = AA+1
#return (((a-a**3)*k0 - (b-b**3)*k1)/6.0*h*h \
#- (y0*a - y1*b) for a,b,k0,k1,y,y1 in zip(AA,BB,k[:-1],k[1:],yData[:.1],yData[1:]))
def evalSpline_old2(xData,yData,k,xx):
y = np.empty_like(xx)
iLeft = 0
iRight = len(xData)- 1
m=-1
for x in xx:
m+=1
while x >= xData[iLeft] and iLeft<iRight: iLeft += 1
i=iLeft-1
h = xData[i] - xData[i+1]
A = (x - xData[i+1])/h
B = (x - xData[i])/h
y[m]= ((A**3 - A)*k[i] - (B**3 - B)*k[i+1])/6.0*h*h \
+ (yData[i]*A - yData[i+1]*B)
return y
def evalSpline_old(xData,yData,k,xx):
def findSegment(xData,x,i):
iLeft = i
iRight = len(xData)- 1
while 1: #Bisection
if (iRight-iLeft) <= 1: return iLeft
i =(iLeft + iRight)/2
if x < xData[i]: iRight = i
else: iLeft = i
yy = []
i = 0
for x in xx:
i = findSegment(xData,x,i)
h = xData[i] - xData[i+1]
y = ((x - xData[i+1])**3/h - (x - xData[i+1])*h)*k[i]/6.0 \
- ((x - xData[i])**3/h - (x - xData[i])*h)*k[i+1]/6.0 \
+ (yData[i]*(x - xData[i+1]) \
- yData[i+1]*(x - xData[i]))/h
yy.append(y)
if i<10: print(i,y,x, k[i],x - xData[i])
return np.array(yy)
def cubic(x):
"""A cubic B-spline.
This is a special case of `bspline`, and equivalent to ``bspline(x, 3)``.
"""
ax = abs(asarray(x))
res = zeros_like(ax)
cond1 = less(ax, 1)
if cond1.any():
ax1 = ax[cond1]
res[cond1] = 2.0 / 3 - 1.0 / 2 * ax1 ** 2 * (2 - ax1)
cond2 = ~cond1 & less(ax, 2)
if cond2.any():
ax2 = ax[cond2]
res[cond2] = 1.0 / 6 * (2 - ax2) ** 3
return res
def csp_eval(cj, newx, dx=1.0, x0=0):
"""Evaluate a spline at the new set of points.
`dx` is the old sample-spacing while `x0` was the old origin. In
other-words the old-sample points (knot-points) for which the `cj`
represent spline coefficients were at equally-spaced points of:
oldx = x0 + j*dx j=0...N-1, with N=len(cj)
Edges are handled using mirror-symmetric boundary conditions.
"""
newx = (asarray(newx) - x0) / float(dx)
res = zeros_like(newx)
if res.size == 0:
return res
N = len(cj)
cond1 = newx < 0
cond2 = newx > (N - 1)
cond3 = ~(cond1 | cond2)
# handle general mirror-symmetry
res[cond1] = csp_eval(cj, -newx[cond1])
res[cond2] = csp_eval(cj, 2 * (N - 1) - newx[cond2])
newx = newx[cond3]
if newx.size == 0:
return res
result = zeros_like(newx)
jlower = floor(newx - 2).astype(int) + 1
for i in range(4):
thisj = jlower + i
indj = thisj.clip(0, N - 1) # handle edge cases
result += cj[indj] * cubic(newx - thisj)
res[cond3] = result
return res
|
mzechmeisterREPO_NAMEservalPATH_START.@serval_extracted@serval-master@src@cubicSpline.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "mikekatz04/BOWIE",
"repo_path": "BOWIE_extracted/BOWIE-master/snr_calculator_folder/setup.py",
"type": "Python"
}
|
import os
import shutil
from os.path import join as pjoin
from setuptools import setup
from distutils.core import Extension
from Cython.Build import cythonize
import numpy
# Obtain the numpy include directory. This logic works across numpy versions.
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = numpy.get_numpy_include()
lib_gsl_dir = "/opt/local/lib"
include_gsl_dir = "/opt/local/include"
extmodule1 = Extension(
"PhenomD",
libraries=["gsl", "gslcblas"],
library_dirs=[lib_gsl_dir],
sources=["gwsnrcalc/utils/src/phenomd.c", "gwsnrcalc/utils/PhenomD.pyx"],
include_dirs=["gwsnrcalc/utils/src/", numpy.get_include()],
)
extmodule2 = Extension(
"Csnr",
libraries=["gsl", "gslcblas"],
library_dirs=[lib_gsl_dir],
sources=["gwsnrcalc/utils/src/snr.c", "gwsnrcalc/utils/Csnr.pyx"],
include_dirs=["gwsnrcalc/utils/src/", numpy.get_include()],
)
extensions = cythonize([extmodule1, extmodule2])
with open("README.rst", "r") as fh:
long_description = fh.read()
setup(
name="gwsnrcalc",
version="1.1.0",
description="Gravitational waveforms and snr.",
long_description=long_description,
long_description_content_type="text/x-rst",
author="Michael Katz",
author_email="mikekatz04@gmail.com",
url="https://github.com/mikekatz04/BOWIE/snr_calculator_folder",
packages=[
"gwsnrcalc",
"gwsnrcalc.utils",
"gwsnrcalc.utils.noise_curves",
"gwsnrcalc.genconutils",
],
ext_modules=extensions,
py_modules=["gwsnrcalc.gw_snr_calculator", "gwsnrcalc.generate_contour_data"],
install_requires=["numpy", "scipy", "astropy", "h5py"],
include_package_data=True,
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
],
)
|
mikekatz04REPO_NAMEBOWIEPATH_START.@BOWIE_extracted@BOWIE-master@snr_calculator_folder@setup.py@.PATH_END.py
|
{
"filename": "exosystem_lib.py",
"repo_name": "subisarkar/JexoSim",
"repo_path": "JexoSim_extracted/JexoSim-master/jexosim/lib/exosystem_lib.py",
"type": "Python"
}
|
"""
JexoSim 2.0
Exosystem and related library
"""
import numpy as np
from jexosim.lib import jexosim_lib
from jexosim.lib.jexosim_lib import jexosim_msg, jexosim_plot
from jexosim.classes.sed import Sed
from pytransit import QuadraticModel
import copy
import matplotlib.pyplot as plt
def get_light_curve(opt, planet_sed, wavelength, obs_type):
wavelength = wavelength.value
timegrid = opt.z_params[0]
t0 = opt.z_params[1]
per = opt.z_params[2]
ars = opt.z_params[3]
inc = opt.z_params[4]
ecc = opt.z_params[5]
omega = opt.z_params[6]
if opt.observation.obs_type.val == 2:
opt.timeline.useLDC.val = 0
if opt.timeline.useLDC.val == 0: # linear ldc
jexosim_msg('LDCs set to zero', 1)
u0 = np.zeros(len(wavelength)); u1 = np.zeros(len(wavelength))
ldc = np.vstack((wavelength,u0,u1))
elif opt.timeline.useLDC.val == 1: # use to get ldc from filed values
u0,u1 = getLDC_interp(opt, opt.planet.planet, wavelength)
ldc = np.vstack((wavelength,u0,u1))
gamma = np.zeros((ldc.shape[1],2))
gamma[:,0] = ldc[1]
gamma[:,1] = ldc[2]
jexosim_plot('ldc', opt.diagnostics, ydata =ldc[1])
jexosim_plot('ldc', opt.diagnostics, ydata =ldc[2])
tm = QuadraticModel(interpolate=False)
tm.set_data(timegrid)
lc = np.zeros((len(planet_sed), len(timegrid) ))
if obs_type == 1: #primary transit
for i in range(len(planet_sed)):
k = np.sqrt(planet_sed[i]).value
lc[i, ...]= tm.evaluate(k=k, ldc=gamma[i, ...], t0=t0, p=per, a=ars, i=inc, e=ecc, w=omega)
jexosim_plot('light curve check', opt.diagnostics, ydata = lc[i, ...] )
elif obs_type == 2: # secondary eclipse
for i in range(len(planet_sed)):
k = np.sqrt(planet_sed[i]) # planet star radius ratio
lc_base = tm.evaluate(k=k, ldc=[0,0], t0=t0, p=per, a=ars, i=inc, e=ecc, w=omega)
jexosim_plot('light curve check', opt.diagnostics, ydata = lc_base )
# f_e = (planet_sed[i] + ( lc_base -1.0))/planet_sed[i]
# lc[i, ...] = 1.0 + f_e*(planet_sed[i])
lc[i,...] = lc_base + planet_sed[i]
jexosim_plot('light curve check', opt.diagnostics, ydata = lc[i, ...] )
return lc, ldc
def getLDC_interp(opt, planet, wl):
jexosim_msg ('Calculating LDC from pre-installed files', 1)
T = planet.star.T.value
if T < 3000.0: # temp fix as LDCs not available for T< 3000 K
T=3000.0
logg = planet.star.logg
jexosim_msg ("T_s, logg>>>> %s %s"%(T, logg ), opt.diagnostics)
folder = '%s/archive/LDC'%(opt.jexosim_path)
t_base = np.int(T/100.)*100
if T>=t_base+100:
t1 = t_base+100
t2 = t_base+200
else:
t1 = t_base
t2 = t_base+100
logg_base = np.int(logg)
if logg>= logg_base+0.5:
logg1 = logg_base+0.5
logg2 = logg_base+1.0
else:
logg1 = logg_base
logg2 = logg_base+0.5
logg1 = np.float(logg1)
logg2 = np.float(logg2)
#==============================================================================
# interpolate between temperatures assuming logg1
#==============================================================================
u0_a = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t1, logg1))[:,1]
u0_b = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t2, logg1))[:,1]
w2 = 1.0-abs(t2-T)/100.
w1 = 1.0-abs(t1-T)/100.
u0_1 = w1*u0_a + w2*u0_b
u1_a = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t1, logg1))[:,2]
u1_b = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t2, logg1))[:,2]
w2 = 1.0-abs(t2-T)/100.
w1 = 1.0-abs(t1-T)/100.
u1_1 = w1*u1_a + w2*u1_b
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u0_a, marker='b-')
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u0_b, marker='r-')
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u0_1, marker='g-')
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u1_a, marker='b-')
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u1_b, marker='r-')
jexosim_plot('ldc interp logg1', opt.diagnostics, ydata=u1_1, marker='g-')
ldc_wl = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t1, logg1))[:,0]
#==============================================================================
# interpolate between temperatures assuming logg2
#==============================================================================
u0_a = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t1, logg2))[:,1]
u0_b = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t2, logg2))[:,1]
w2 = 1.0-abs(t2-T)/100.
w1 = 1.0-abs(t1-T)/100.
u0_2 = w1*u0_a + w2*u0_b
u1_a = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t1, logg2))[:,2]
u1_b = np.loadtxt('%s/T=%s_logg=%s_z=0.txt'%(folder,t2, logg2))[:,2]
w2 = 1.0-abs(t2-T)/100.
w1 = 1.0-abs(t1-T)/100.
u1_2 = w1*u1_a + w2*u1_b
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u0_a, marker='b-')
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u0_b, marker='r-')
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u0_2, marker='g-')
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u1_a, marker='b-')
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u1_b, marker='r-')
jexosim_plot('ldc interp logg2', opt.diagnostics, ydata=u1_2, marker='g-')
#==============================================================================
# interpolate between logg
#==============================================================================
w2 = 1.0-abs(logg2-logg)/.5
w1 = 1.0-abs(logg1-logg)/.5
u0 = w1*u0_1 + w2*u0_2
u1 = w1*u1_1 + w2*u1_2
jexosim_plot('ldc interp', opt.diagnostics, ydata=u0_1, marker='b-')
jexosim_plot('ldc interp', opt.diagnostics, ydata=u0_2, marker='r-')
jexosim_plot('ldc interp', opt.diagnostics, ydata=u0, marker='g-')
jexosim_plot('ldc interp', opt.diagnostics, ydata=u1_1, marker='b-')
jexosim_plot('ldc interp', opt.diagnostics, ydata=u1_2, marker='r-')
jexosim_plot('ldc interp', opt.diagnostics, ydata=u1, marker='g-')
#==============================================================================
# interpolate to new wl grid
#==============================================================================
u0_final = np.interp(wl,ldc_wl,u0)
u1_final = np.interp(wl,ldc_wl,u1)
jexosim_plot('ldc final', opt.diagnostics, xdata = ldc_wl, ydata=u0, marker='ro', alpha=0.1)
jexosim_plot('ldc final', opt.diagnostics, xdata = ldc_wl, ydata=u1, marker='bo', alpha=0.1)
jexosim_plot('ldc final', opt.diagnostics, xdata = wl, ydata=u0_final, marker='rx')
jexosim_plot('ldc final', opt.diagnostics, xdata = wl, ydata=u1_final, marker='bx')
return u0_final, u1_final
def calc_T14(inc, a, per, Rp, Rs):
b = np.cos(inc)*a/Rs
rat = Rp/Rs
t14 = per/np.pi* Rs/a * np.sqrt((1+rat)**2 - b**2)
return t14
def get_it_spectrum(opt):
wl_lo = opt.channel.pipeline_params.wavrange_lo.val
wl_hi = opt.channel.pipeline_params.wavrange_hi.val
wl = opt.star.sed.wl
planet_wl = opt.planet.sed.wl
# CHANGE INTRODUCED
# If the star is downsampled here it can cause inconsistencies in the representation of the source spectrum
# Hence I err on the side of upsampling the planet spectrum to the star resolution, rather than downsampling
# the star spectrum to the planet spectrum resolution.
# The impact of this change on the final planet spectrum is minimal
# R = wl/np.gradient((wl))
# R_planet = planet_wl/np.gradient((planet_wl))
# # select onyl wavelengths in range for channel
# idx = np.argwhere((wl.value>= wl_lo) & (wl.value<= wl_hi)).T[0]
# idx2 = np.argwhere((planet_wl.value>= wl_lo) & (planet_wl.value<= wl_hi)).T[0]
# # rebin to lower resolution spectrum
# if R_planet[idx2].min() < R[idx].min():
# opt.star.sed.rebin(planet_wl)
# else:
# opt.planet.sed.rebin(wl)
opt.planet.sed.rebin(wl)
# now make in-transit stellar spectrum
star_sed_it = opt.star.sed.sed*(1-opt.planet.sed.sed)
opt.star.sed_it = Sed(opt.star.sed.wl, star_sed_it)
return opt
return opt
|
subisarkarREPO_NAMEJexoSimPATH_START.@JexoSim_extracted@JexoSim-master@jexosim@lib@exosystem_lib.py@.PATH_END.py
|
{
"filename": "Test HIPSTER-checkpoint.ipynb",
"repo_name": "oliverphilcox/HIPSTER",
"repo_path": "HIPSTER_extracted/HIPSTER-master/.ipynb_checkpoints/Test HIPSTER-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
oliverphilcoxREPO_NAMEHIPSTERPATH_START.@HIPSTER_extracted@HIPSTER-master@.ipynb_checkpoints@Test HIPSTER-checkpoint.ipynb@.PATH_END.py
|
|
{
"filename": "_tickfont.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/surface/colorbar/_tickfont.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Tickfont(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "surface.colorbar"
_path_str = "surface.colorbar.tickfont"
_valid_props = {
"color",
"family",
"lineposition",
"shadow",
"size",
"style",
"textcase",
"variant",
"weight",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# family
# ------
@property
def family(self):
"""
HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans", "Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow",
"Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
Returns
-------
str
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# lineposition
# ------------
@property
def lineposition(self):
"""
Sets the kind of decoration line(s) with text, such as an
"under", "over" or "through" as well as combinations e.g.
"under+over", etc.
The 'lineposition' property is a flaglist and may be specified
as a string containing:
- Any combination of ['under', 'over', 'through'] joined with '+' characters
(e.g. 'under+over')
OR exactly one of ['none'] (e.g. 'none')
Returns
-------
Any
"""
return self["lineposition"]
@lineposition.setter
def lineposition(self, val):
self["lineposition"] = val
# shadow
# ------
@property
def shadow(self):
"""
Sets the shape and color of the shadow behind text. "auto"
places minimal shadow and applies contrast text font color. See
https://developer.mozilla.org/en-US/docs/Web/CSS/text-shadow
for additional options.
The 'shadow' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["shadow"]
@shadow.setter
def shadow(self, val):
self["shadow"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
Returns
-------
int|float
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
Returns
-------
Any
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# textcase
# --------
@property
def textcase(self):
"""
Sets capitalization of text. It can be used to make text appear
in all-uppercase or all-lowercase, or with each word
capitalized.
The 'textcase' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'word caps', 'upper', 'lower']
Returns
-------
Any
"""
return self["textcase"]
@textcase.setter
def textcase(self, val):
self["textcase"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps', 'all-small-caps',
'all-petite-caps', 'petite-caps', 'unicase']
Returns
-------
Any
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [1, 1000]
OR exactly one of ['normal', 'bold'] (e.g. 'bold')
Returns
-------
int
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
"""
def __init__(
self,
arg=None,
color=None,
family=None,
lineposition=None,
shadow=None,
size=None,
style=None,
textcase=None,
variant=None,
weight=None,
**kwargs,
):
"""
Construct a new Tickfont object
Sets the color bar's tick label font
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.surface.colorbar.Tickfont`
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
Returns
-------
Tickfont
"""
super(Tickfont, self).__init__("tickfont")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.surface.colorbar.Tickfont
constructor must be a dict or
an instance of :class:`plotly.graph_objs.surface.colorbar.Tickfont`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("lineposition", None)
_v = lineposition if lineposition is not None else _v
if _v is not None:
self["lineposition"] = _v
_v = arg.pop("shadow", None)
_v = shadow if shadow is not None else _v
if _v is not None:
self["shadow"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("textcase", None)
_v = textcase if textcase is not None else _v
if _v is not None:
self["textcase"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@surface@colorbar@_tickfont.py@.PATH_END.py
|
{
"filename": "parameter_file_reference.md",
"repo_name": "yuguangchen1/KcwiKit",
"repo_path": "KcwiKit_extracted/KcwiKit-master/kcwikit/docs/parameter_file_reference.md",
"type": "Markdown"
}
|
# Complete Reference of the Parameter File
Here is the complete reference on the parameter file. However, not all of the parameters are commonly used. An example for common usage is provided [here](../examples/q2343-BX610.par).
Some parameters can be overridden by manually setting them when calling the functions. For example, `kcwi.kcwi_align(..., search_size=15)` will manually increase the alignment box to 15 arcsec.
```
dimension 100 100 # set up the number of pixels in the X and Y directions
orientation 0 # PA of the frame, starting north and increasing counter-clockwise
xpix 0.3 # pixel scale (arcsec) in the X direction
ypix 0.3 # pixel scale (arcsec) in the Y direction
wavebin # wavelength range in which the white-light images are created, most useful for the red channel
drizzle # drizzle factor, default 0.7
align_box 45 37 65 65 # x0, y0, x1, y1 of the alignment box
align_dimension # number of pixels specifically for the alignment frame
align_xpix # pixel scale (arcsec) in the X direction, specifically for the alignment frame
align_ypix # pixel scale (arcsec) in the Y direction, specifically for the alignment frame
align_orientation # PA of the frame, specifically for the alignment frame
align_search_size # half width of the search size, default 10 pixels
align_conv_filter # size of the convolution filter when searching for the local maximum in cross correlation, default 2 pixels
align_upfactor # upscaling factor for finer local maximum searching, default 10 times.
align_ad # RA, Dec of the center of the alignment frame, default center of the first frame
background_subtraction # conducting background subtraction for alignment? Default false
background_level # background level for background subtraction, default median of the frame
stack_dimension # number of pixels, specifically for the stacking frame
stack_xpix # X pixel scale, specifically for the stacking frame
stack_ypix # Y pixel scale, specifically for the stacking frame
stack_orientation # PA of the frmae, specifically for the stacking frame
stack_ad # RA, Dec of the center of the stacking frame, default center of the first frame
wave_ref # CRPIX3 and CRVAL3 of the wavelength grid of the final cube. Default equal to the first frame
nwave # number of wavelength pixels of the final cube. Default equal to the first frame
dwave # pixel scale (Angstrom) in the wavelength direction of the final cube. Default equal to the first frame
ref_xy 51 52 # the X, Y pixel location of the reference object for astrometry
ref_ad 356.53929 12.822 # the RA, Dec (in degrees) of the reference object for astrometry
ref_fn /path/to/image # an external reference FITS image for astrometry
ref_search_size # search size for astrometry, see align_search_size
ref_conv_filter # convolution filter size for astrometry, see align_conv_filter
ref_upfactor # upscaling factor for astrometry, see align_upfactor
ref_nocrl # if set to nonzero, astrometry is skipped
```
|
yuguangchen1REPO_NAMEKcwiKitPATH_START.@KcwiKit_extracted@KcwiKit-master@kcwikit@docs@parameter_file_reference.md@.PATH_END.py
|
{
"filename": "_tickformat.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/icicle/marker/colorbar/_tickformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="tickformat", parent_name="icicle.marker.colorbar", **kwargs
):
super(TickformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@icicle@marker@colorbar@_tickformat.py@.PATH_END.py
|
{
"filename": "arith.md",
"repo_name": "myrafproject/myrafproject",
"repo_path": "myrafproject_extracted/myrafproject-main/example/command_line/arith.md",
"type": "Markdown"
}
|
# arith
`arith` does image arithmetic on FITS files.
usage: im arith [-h] file operator other output
## Positional Arguments:
- **file** A file path or pattern (e.g., "*.fits")
- **operator** Operator: +, -, /, *, **, ^
- **other** FITS file or number to operate with
- **output** Output directory
## Options:
- -h, --help Show this help message and exit
|
myrafprojectREPO_NAMEmyrafprojectPATH_START.@myrafproject_extracted@myrafproject-main@example@command_line@arith.md@.PATH_END.py
|
{
"filename": "_y.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/box/_y.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class YValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="y", parent_name="box", **kwargs):
super(YValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc+clearAxisTypes"),
role=kwargs.pop("role", "data"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@box@_y.py@.PATH_END.py
|
{
"filename": "corruptor.py",
"repo_name": "OxfordSKA/OSKAR",
"repo_path": "OSKAR_extracted/OSKAR-master/python/examples/corruptor.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""Simulates visibilities using OSKAR and corrupts them.
Usage: python corruptor.py <oskar_sim_interferometer.ini>
The command line arguments are:
- oskar_sim_interferometer.ini: Path to a settings file for the
oskar_sim_interferometer app.
"""
from __future__ import print_function
import sys
import oskar
class Corruptor(oskar.Interferometer):
"""Corrupts visibilities on-the-fly from OSKAR.
"""
def __init__(self, precision=None, oskar_settings=None):
oskar.Interferometer.__init__(self, precision, oskar_settings)
# Do any other initialisation here...
print("Initialising...")
def finalise(self):
"""Called automatically by the base class at the end of run()."""
oskar.Interferometer.finalise(self)
# Do any other finalisation here...
print("Finalising...")
def process_block(self, block, block_index):
"""Corrupts the visibility block amplitude data.
Args:
block (oskar.VisBlock): The block to be processed.
block_index (int): The index of the visibility block.
"""
# Get handles to visibility block data as numpy arrays.
uu = block.baseline_uu_metres()
vv = block.baseline_vv_metres()
ww = block.baseline_ww_metres()
amp = block.cross_correlations()
# Corrupt visibility amplitudes in the block here as needed
# by messing with amp array.
# uu, vv, ww have dimensions (num_times,num_baselines)
# amp has dimensions (num_times,num_channels,num_baselines,num_pols)
print("Processing block {}/{} (time index {}-{})...".
format(block_index + 1,
self.num_vis_blocks,
block.start_time_index,
block.start_time_index + block.num_times - 1))
# Simplest example: amp *= 2.0
amp *= 2.0
# Write corrupted visibilities in the block to file(s).
self.write_block(block, block_index)
def main():
"""Main function for visibility corruptor."""
# Check command line arguments.
if len(sys.argv) < 2:
raise RuntimeError('Usage: python corruptor.py '
'<oskar_sim_interferometer.ini>')
# Load the OSKAR settings INI file for the application.
settings = oskar.SettingsTree('oskar_sim_interferometer', sys.argv[-1])
# Set up the corruptor and run it (see method, above).
corruptor = Corruptor(oskar_settings=settings)
corruptor.run()
if __name__ == '__main__':
main()
|
OxfordSKAREPO_NAMEOSKARPATH_START.@OSKAR_extracted@OSKAR-master@python@examples@corruptor.py@.PATH_END.py
|
{
"filename": "calibrators.py",
"repo_name": "TianlaiProject/tlpipe",
"repo_path": "tlpipe_extracted/tlpipe-master/tlpipe/cal/calibrators.py",
"type": "Python"
}
|
import numpy as np
import ephem
import aipy as a
# 20 sources from Perley and Butler, 2017, An accurate flux density scale from 50 MHz to 50 GHz
src_data = {
# key name RA DEC a0 a1 a2 a3 a4 a5 angle_size
'j0133': ('J0133-3629', 'xx:xx:xxx', 'xx:xx:xxx', 1.0440, -0.6619, -0.2252, 0.0, 0.0, 0.0, np.radians(14.0 / 60)),
'3c48' : ('3C48', 'xx:xx:xxx', 'xx:xx:xxx', 1.3253, -0.7553, -0.1914, 0.0498, 0.0, 0.0, np.radians(1.2 / 3600)),
'for' : ('Fornax A', '03:22:41.7', '-37:12:30', 2.2175, -0.6606, 0.0, 0.0, 0.0, 0.0, np.radians(55.0 / 60)),
'3c123': ('3C123', 'xx:xx:xxx', 'xx:xx:xxx', 1.8017, -0.7884, -0.1035, -0.0248, 0.0090, 0.0, np.radians(44.0 / 3600)),
'j0444': ('J0444-2809', 'xx:xx:xxx', 'xx:xx:xxx', 0.9710, -0.8938, -0.1176, 0.0, 0.0, 0.0, np.radians(2.0 / 60)),
'3c138': ('3C138', 'xx:xx:xxx', 'xx:xx:xxx', 1.0088, -0.4981, -0.1552, -0.0102, 0.0223, 0.0, np.radians(0.7 / 3600)),
'pic' : ('Pictor A', '05:19:49.7', '-45:46:45', 1.9380, -0.7470, -0.0739, 0.0, 0.0, 0.0, np.radians(8.3 / 60)),
'crab' : ('Taurus A', '05:34:32.0', '+22:00:52', 2.9516, -0.2173, -0.0473, -0.0674, 0.0, 0.0, np.radians(0.0)),
'3c147': ('3C147', 'xx:xx:xxx', 'xx:xx:xxx', 1.4516, -0.6961, -0.2007, 0.0640, -0.0464, 0.0289, np.radians(0.9 / 3600)),
'3c196': ('3C196', 'xx:xx:xxx', 'xx:xx:xxx', 1.2872, -0.8530, -0.1534, -0.0200, 0.0201, 0.0, np.radians(7.0 / 3600)),
'hyd' : ('Hydra A', '09:18:05.7', '-12:05:44', 1.7795, -0.9176, -0.0843, -0.0139, 0.0295, 0.0, np.radians(8.0 / 60)),
'vir' : ('Virgo A', '12:30:49.4', '+12:23:28', 2.4466, -0.8116, -0.0483, 0.0, 0.0, 0.0, np.radians(14.0 / 60)),
'3c286': ('3C286', 'xx:xx:xxx', 'xx:xx:xxx', 1.2481, -0.4507, -0.1798, 0.0357, 0.0, 0.0, np.radians(3.0 / 3600)),
'3c295': ('3C295', 'xx:xx:xxx', 'xx:xx:xxx', 1.4701, -0.7658, -0.2780, -0.0347, 0.0399, 0.0, np.radians(5.0 / 3600)),
'her' : ('Hercules A', '16:51:08.15', '4:59:33.3', 1.8298, -0.1247, -0.0951, 0.0, 0.0, 0.0, np.radians(3.1 / 60)),
'3c353': ('3C353', 'xx:xx:xxx', 'xx:xx:xxx', 1.8627, -0.6938, -0.0998, -0.0732, 0.0, 0.0, np.radians(5.3)),
'3c380': ('3C380', 'xx:xx:xxx', 'xx:xx:xxx', 1.2320, -0.7909, 0.0947, 0.0976, -0.1794, -0.1566, np.radians(0.0)),
'cyg' : ('Cygnus A', '19:59:28.3', '+40:44:02', 3.3498, -1.0022, -0.2246, 0.0227, 0.0425, 0.0, np.radians(2.0 / 60)),
'3c444': ('3C444', 'xx:xx:xxx', 'xx:xx:xxx', 1.1064, -1.0052, -0.0750, -0.0767, 0.0, 0.0, np.radians(2.0 / 60)),
'cas' : ('Cassiopeia A', '23:23:27.94', '+58:48:42.4', 3.3584, -0.7518, -0.0347, -0.0705, 0.0, 0.0, np.radians(0.0)),
}
class RadioBody(object):
"""A celestial source."""
def __init__(self, name, poly_coeffs, ionref, srcshape):
self.src_name = name
self.poly_coeffs = poly_coeffs
self.ionref = list(ionref)
self.srcshape = list(srcshape)
def __str__(self):
return "%s" % self.src_name
def compute(self, observer):
"""Update coordinates relative to the provided `observer`.
Must be called at each time step before accessing information.
"""
# Generate a map for projecting baselines to uvw coordinates
self.map = a.coord.eq2top_m(observer.sidereal_time() - self.ra, self.dec)
def get_crds(self, crdsys, ncrd=3):
"""Return the coordinates of this location in the desired coordinate
system ('eq','top') in the current epoch.
If ncrd=2, angular coordinates (ra/dec or az/alt) are returned,
and if ncrd=3, xyz coordinates are returned.
"""
assert(crdsys in ('eq','top'))
assert(ncrd in (2,3))
if crdsys == 'eq':
if ncrd == 2:
return (self.ra, self.dec)
return a.coord.radec2eq((self.ra, self.dec))
else:
if ncrd == 2:
return (self.az, self.alt)
return a.coord.azalt2top((self.az, self.alt))
def get_jys(self, afreq):
"""Update fluxes at the given frequencies.
Parameters
----------
afreq : float or float array
frequency in GHz.
"""
log_nv = np.log10(afreq)
a0, a1, a2, a3, a4, a5 = self.poly_coeffs
log_S = a0 + a1 * log_nv + a2 * log_nv**2 + a3 * log_nv**3 + a4 * log_nv**4 + a5 * log_nv**5
return 10**log_S
class RadioFixedBody(ephem.FixedBody, RadioBody):
"""A source at fixed RA,DEC. Combines ephem.FixedBody with RadioBody."""
def __init__(self, ra, dec, poly_coeffs, name='', epoch=ephem.J2000, ionref=(0.0, 0.0), srcshape=(0.0, 0.0, 0.0), **kwargs):
RadioBody.__init__(self, name, poly_coeffs, ionref, srcshape)
ephem.FixedBody.__init__(self)
self._ra, self._dec = ra, dec
self._epoch = epoch
def __str__(self):
if self._dec<0:
return RadioBody.__str__(self) + '\t' + str(self._ra) +'\t'+ str(self._dec)
else:
return RadioBody.__str__(self) + '\t' + str(self._ra) +'\t'+'+' + str(self._dec)
def compute(self, observer):
ephem.FixedBody.compute(self, observer)
RadioBody.compute(self, observer)
_cat = None
def get_src(name):
"""Return a source in `src_data` withe key == `name`."""
name, ra, dec, a0, a1, a2, a3, a4, a5, srcshape = src_data[name]
try:
len(srcshape)
except(TypeError):
srcshape = (srcshape, srcshape, 0.)
return RadioFixedBody(ra, dec, poly_coeffs=(a0, a1, a2, a3, a4, a5), name=name, srcshape=srcshape)
def get_srcs(srcs=None, cutoff=None):
global _cat
if _cat is None:
_cat = a.fit.SrcCatalog()
srclist = []
for s in src_data:
srclist.append(get_src(s))
_cat.add_srcs(srclist)
if srcs is None:
if cutoff is None:
srcs = _cat.keys()
else:
cut, fq = cutoff
fq = n.array([fq])
for s in _misccat.keys():
_cat[s].update_jys(fq)
srcs = [ s for s in _misccat.keys() if _cat[s].jys[0] > cut ]
srclist = []
for s in srcs:
try:
srclist.append(_cat[s])
except(KeyError):
pass
return srclist
if __name__ == '__main__':
name = 'cyg'
s = get_src(name)
print s
freq = 0.75 # MHz
print s.get_jys(freq)
freqs = np.logspace(-1.0, 1.0)
jys = s.get_jys(freqs)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure()
plt.loglog(freqs, jys)
plt.savefig('cyg_flux.png')
plt.close()
srclist, cutoff, catalogs = a.scripting.parse_srcs(name, 'misc')
cat = a.src.get_catalog(srclist, cutoff, catalogs)
s1 = cat.values()[0]
s1.update_jys(freq)
print s1.get_jys()
|
TianlaiProjectREPO_NAMEtlpipePATH_START.@tlpipe_extracted@tlpipe-master@tlpipe@cal@calibrators.py@.PATH_END.py
|
{
"filename": "_widthsrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatterternary/marker/line/_widthsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WidthsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="widthsrc", parent_name="scatterternary.marker.line", **kwargs
):
super(WidthsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatterternary@marker@line@_widthsrc.py@.PATH_END.py
|
{
"filename": "GalRotpy.ipynb",
"repo_name": "andresGranadosC/GalRotpy",
"repo_path": "GalRotpy_extracted/GalRotpy-master/notebook/GalRotpy.ipynb",
"type": "Jupyter Notebook"
}
|
```python
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons, TextBox # Matplotlib widgets
import matplotlib.pylab as plt # Plotting interface
import numpy as np
from galpy.potential import MiyamotoNagaiPotential, NFWPotential, RazorThinExponentialDiskPotential, BurkertPotential # GALPY potentials
from galpy.potential import calcRotcurve # composed rotation curve calculation for plotting
from astropy import units # Physical/real units data managing
from astropy import table as Table # For fast and easy reading / writing with tables using numpy library
import emcee
import corner
import time
import pandas as pd
import multiprocessing as mp
from scipy.optimize import fsolve
import ipywidgets as widgets
```
galpyWarning: libgalpy C extension module not loaded, because of error 'dlopen(/Library/Python/3.7/site-packages/libgalpy.cpython-37m-darwin.so, 6): Library not loaded: @rpath/libgsl.25.dylib
Referenced from: /Library/Python/3.7/site-packages/libgalpy.cpython-37m-darwin.so
Reason: image not found'
```python
def boolString_to_bool(boolString):
if boolString == 'True':
return True
elif boolString == 'False':
return False
else:
return None
```
```python
init_guess_params = Table.Table.read('../M33_guess_params.txt', format='ascii.tab')
```
```python
init_guess_params
```
<i>Table length=6</i>
<table id="table4790125960" class="table-striped table-bordered table-condensed">
<thead><tr><th>component</th><th>mass</th><th>a (kpc)</th><th>b (kpc)</th><th>checked</th></tr></thead>
<thead><tr><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>str5</th></tr></thead>
<tr><td>BULGE</td><td>110000000.0</td><td>0.0</td><td>0.495</td><td>False</td></tr>
<tr><td>THIN DISC</td><td>38837296969.03567</td><td>10.069858729947157</td><td>2.499954901776149</td><td>True</td></tr>
<tr><td>THICK DISC</td><td>39000000000.0</td><td>2.6</td><td>0.8</td><td>False</td></tr>
<tr><td>EXP. DISC</td><td>500.0</td><td>5.3</td><td>0.0</td><td>False</td></tr>
<tr><td>DARK HALO</td><td>1196921394849.7383</td><td>18.682199726086495</td><td>0.0</td><td>True</td></tr>
<tr><td>BURKERT HALO</td><td>8000000.0</td><td>20.0</td><td>0.0</td><td>False</td></tr>
</table>
```python
c_bulge, amp1, a1, b1, include_bulge = init_guess_params[0]
c_tn, amp2, a2, b2, include_tn = init_guess_params[1]
c_tk, amp3, a3, b3, include_tk = init_guess_params[2]
c_ex, amp4, h_r, vertical_ex, include_ex = init_guess_params[3]
c_dh, amp5, a5, b5, include_dh = init_guess_params[4]
c_bh, amp6, a6, b6, include_bh = init_guess_params[5]
```
```python
visibility = [ boolString_to_bool(include_bulge), boolString_to_bool(include_tn), boolString_to_bool(include_tk), boolString_to_bool(include_ex), boolString_to_bool(include_dh), boolString_to_bool(include_bh)]
```
```python
input_params=Table.Table.read('../input_params.txt', format='ascii.tab') # Initial parameters
input_params
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-2-2b535d68edb6> in <module>
----> 1 input_params=Table.Table.read('../input_params.txt', format='ascii.tab') # Initial parameters
2 input_params
/Library/Python/3.7/site-packages/astropy/table/connect.py in __call__(self, *args, **kwargs)
50 def __call__(self, *args, **kwargs):
51 cls = self._cls
---> 52 out = registry.read(cls, *args, **kwargs)
53
54 # For some readers (e.g., ascii.ecsv), the returned `out` class is not
/Library/Python/3.7/site-packages/astropy/io/registry.py in read(cls, format, *args, **kwargs)
521
522 reader = get_reader(format, cls)
--> 523 data = reader(*args, **kwargs)
524
525 if not isinstance(data, cls):
/Library/Python/3.7/site-packages/astropy/io/ascii/connect.py in io_read(format, filename, **kwargs)
16 format = re.sub(r'^ascii\.', '', format)
17 kwargs['format'] = format
---> 18 return read(filename, **kwargs)
19
20
/Library/Python/3.7/site-packages/astropy/io/ascii/ui.py in read(table, guess, **kwargs)
285 # through below to the non-guess way so that any problems result in a
286 # more useful traceback.
--> 287 dat = _guess(table, new_kwargs, format, fast_reader)
288 if dat is None:
289 guess = False
/Library/Python/3.7/site-packages/astropy/io/ascii/ui.py in _guess(table, read_kwargs, format, fast_reader)
445
446 reader.guessing = True
--> 447 dat = reader.read(table)
448 _read_trace.append({'kwargs': copy.deepcopy(guess_kwargs),
449 'Reader': reader.__class__,
/Library/Python/3.7/site-packages/astropy/io/ascii/fastbasic.py in read(self, table)
115 data_start=self.data_start,
116 fill_extra_cols=self.fill_extra_cols,
--> 117 **self.kwargs)
118 conversion_info = self._read_header()
119 self.check_header()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.CParser.__cinit__()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.CParser.setup_tokenizer()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.FileString.__cinit__()
FileNotFoundError: [Errno 2] No such file or directory: '../input_params.txt'
```python
tt=Table.Table.read('../M31_rot_curve.txt', format='ascii.tab') # Rotation curve
x_offset = 0.0 # It defines a radial coordinate offset as user input
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
# Real data:
r_data=tt['r']-x_offset # The txt file must contain the radial coordinate values in kpc
v_c_data=tt['vel'] # velocity in km/s
v_c_err_data = tt['e_vel'] # and velocity error in km/s
# This loop is needed since galpy fails when r=0 or very close to 0
for i in range(len(r_data)):
if r_data[i]<1e-3:
r_data[i]=1e-3
```
```python
tt
```
<i>Table length=28</i>
<table id="table4731257240" class="table-striped table-bordered table-condensed">
<thead><tr><th>r</th><th>r2</th><th>vel</th><th>e_vel</th></tr></thead>
<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>25.0</td><td>5.68</td><td>235.5</td><td>17.8</td></tr>
<tr><td>30.0</td><td>6.81</td><td>242.9</td><td>0.8</td></tr>
<tr><td>35.0</td><td>7.95</td><td>251.1</td><td>0.7</td></tr>
<tr><td>40.0</td><td>9.08</td><td>262.0</td><td>2.1</td></tr>
<tr><td>45.0</td><td>10.22</td><td>258.9</td><td>6.9</td></tr>
<tr><td>50.0</td><td>11.35</td><td>255.1</td><td>5.7</td></tr>
<tr><td>55.0</td><td>12.49</td><td>251.8</td><td>17.1</td></tr>
<tr><td>60.0</td><td>13.62</td><td>252.1</td><td>7.4</td></tr>
<tr><td>65.0</td><td>14.76</td><td>251.0</td><td>18.6</td></tr>
<tr><td>70.0</td><td>15.89</td><td>245.5</td><td>28.8</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td></tr>
<tr><td>112.5</td><td>25.54</td><td>227.4</td><td>28.8</td></tr>
<tr><td>117.0</td><td>26.56</td><td>225.6</td><td>28.8</td></tr>
<tr><td>121.5</td><td>27.58</td><td>224.4</td><td>28.8</td></tr>
<tr><td>126.0</td><td>28.6</td><td>222.3</td><td>28.8</td></tr>
<tr><td>130.5</td><td>29.62</td><td>222.1</td><td>28.8</td></tr>
<tr><td>135.0</td><td>30.65</td><td>224.9</td><td>28.8</td></tr>
<tr><td>139.5</td><td>31.67</td><td>228.1</td><td>28.8</td></tr>
<tr><td>144.0</td><td>32.69</td><td>231.1</td><td>28.8</td></tr>
<tr><td>148.5</td><td>33.71</td><td>230.4</td><td>28.8</td></tr>
<tr><td>153.0</td><td>34.73</td><td>226.8</td><td>28.8</td></tr>
</table>
```python
lista=np.linspace(0.001, 1.02*np.max(r_data), 10*len(r_data)) # radial coordinate for the rotation curve calculation
lista
```
array([1.00000000e-03, 5.60351254e-01, 1.11970251e+00, 1.67905376e+00,
2.23840502e+00, 2.79775627e+00, 3.35710753e+00, 3.91645878e+00,
4.47581004e+00, 5.03516129e+00, 5.59451254e+00, 6.15386380e+00,
6.71321505e+00, 7.27256631e+00, 7.83191756e+00, 8.39126882e+00,
8.95062007e+00, 9.50997133e+00, 1.00693226e+01, 1.06286738e+01,
1.11880251e+01, 1.17473763e+01, 1.23067276e+01, 1.28660789e+01,
1.34254301e+01, 1.39847814e+01, 1.45441326e+01, 1.51034839e+01,
1.56628351e+01, 1.62221864e+01, 1.67815376e+01, 1.73408889e+01,
1.79002401e+01, 1.84595914e+01, 1.90189427e+01, 1.95782939e+01,
2.01376452e+01, 2.06969964e+01, 2.12563477e+01, 2.18156989e+01,
2.23750502e+01, 2.29344014e+01, 2.34937527e+01, 2.40531039e+01,
2.46124552e+01, 2.51718065e+01, 2.57311577e+01, 2.62905090e+01,
2.68498602e+01, 2.74092115e+01, 2.79685627e+01, 2.85279140e+01,
2.90872652e+01, 2.96466165e+01, 3.02059677e+01, 3.07653190e+01,
3.13246703e+01, 3.18840215e+01, 3.24433728e+01, 3.30027240e+01,
3.35620753e+01, 3.41214265e+01, 3.46807778e+01, 3.52401290e+01,
3.57994803e+01, 3.63588315e+01, 3.69181828e+01, 3.74775341e+01,
3.80368853e+01, 3.85962366e+01, 3.91555878e+01, 3.97149391e+01,
4.02742903e+01, 4.08336416e+01, 4.13929928e+01, 4.19523441e+01,
4.25116953e+01, 4.30710466e+01, 4.36303978e+01, 4.41897491e+01,
4.47491004e+01, 4.53084516e+01, 4.58678029e+01, 4.64271541e+01,
4.69865054e+01, 4.75458566e+01, 4.81052079e+01, 4.86645591e+01,
4.92239104e+01, 4.97832616e+01, 5.03426129e+01, 5.09019642e+01,
5.14613154e+01, 5.20206667e+01, 5.25800179e+01, 5.31393692e+01,
5.36987204e+01, 5.42580717e+01, 5.48174229e+01, 5.53767742e+01,
5.59361254e+01, 5.64954767e+01, 5.70548280e+01, 5.76141792e+01,
5.81735305e+01, 5.87328817e+01, 5.92922330e+01, 5.98515842e+01,
6.04109355e+01, 6.09702867e+01, 6.15296380e+01, 6.20889892e+01,
6.26483405e+01, 6.32076918e+01, 6.37670430e+01, 6.43263943e+01,
6.48857455e+01, 6.54450968e+01, 6.60044480e+01, 6.65637993e+01,
6.71231505e+01, 6.76825018e+01, 6.82418530e+01, 6.88012043e+01,
6.93605556e+01, 6.99199068e+01, 7.04792581e+01, 7.10386093e+01,
7.15979606e+01, 7.21573118e+01, 7.27166631e+01, 7.32760143e+01,
7.38353656e+01, 7.43947168e+01, 7.49540681e+01, 7.55134194e+01,
7.60727706e+01, 7.66321219e+01, 7.71914731e+01, 7.77508244e+01,
7.83101756e+01, 7.88695269e+01, 7.94288781e+01, 7.99882294e+01,
8.05475806e+01, 8.11069319e+01, 8.16662832e+01, 8.22256344e+01,
8.27849857e+01, 8.33443369e+01, 8.39036882e+01, 8.44630394e+01,
8.50223907e+01, 8.55817419e+01, 8.61410932e+01, 8.67004444e+01,
8.72597957e+01, 8.78191470e+01, 8.83784982e+01, 8.89378495e+01,
8.94972007e+01, 9.00565520e+01, 9.06159032e+01, 9.11752545e+01,
9.17346057e+01, 9.22939570e+01, 9.28533082e+01, 9.34126595e+01,
9.39720108e+01, 9.45313620e+01, 9.50907133e+01, 9.56500645e+01,
9.62094158e+01, 9.67687670e+01, 9.73281183e+01, 9.78874695e+01,
9.84468208e+01, 9.90061720e+01, 9.95655233e+01, 1.00124875e+02,
1.00684226e+02, 1.01243577e+02, 1.01802928e+02, 1.02362280e+02,
1.02921631e+02, 1.03480982e+02, 1.04040333e+02, 1.04599685e+02,
1.05159036e+02, 1.05718387e+02, 1.06277738e+02, 1.06837090e+02,
1.07396441e+02, 1.07955792e+02, 1.08515143e+02, 1.09074495e+02,
1.09633846e+02, 1.10193197e+02, 1.10752548e+02, 1.11311900e+02,
1.11871251e+02, 1.12430602e+02, 1.12989953e+02, 1.13549305e+02,
1.14108656e+02, 1.14668007e+02, 1.15227358e+02, 1.15786710e+02,
1.16346061e+02, 1.16905412e+02, 1.17464763e+02, 1.18024115e+02,
1.18583466e+02, 1.19142817e+02, 1.19702168e+02, 1.20261520e+02,
1.20820871e+02, 1.21380222e+02, 1.21939573e+02, 1.22498925e+02,
1.23058276e+02, 1.23617627e+02, 1.24176978e+02, 1.24736330e+02,
1.25295681e+02, 1.25855032e+02, 1.26414384e+02, 1.26973735e+02,
1.27533086e+02, 1.28092437e+02, 1.28651789e+02, 1.29211140e+02,
1.29770491e+02, 1.30329842e+02, 1.30889194e+02, 1.31448545e+02,
1.32007896e+02, 1.32567247e+02, 1.33126599e+02, 1.33685950e+02,
1.34245301e+02, 1.34804652e+02, 1.35364004e+02, 1.35923355e+02,
1.36482706e+02, 1.37042057e+02, 1.37601409e+02, 1.38160760e+02,
1.38720111e+02, 1.39279462e+02, 1.39838814e+02, 1.40398165e+02,
1.40957516e+02, 1.41516867e+02, 1.42076219e+02, 1.42635570e+02,
1.43194921e+02, 1.43754272e+02, 1.44313624e+02, 1.44872975e+02,
1.45432326e+02, 1.45991677e+02, 1.46551029e+02, 1.47110380e+02,
1.47669731e+02, 1.48229082e+02, 1.48788434e+02, 1.49347785e+02,
1.49907136e+02, 1.50466487e+02, 1.51025839e+02, 1.51585190e+02,
1.52144541e+02, 1.52703892e+02, 1.53263244e+02, 1.53822595e+02,
1.54381946e+02, 1.54941297e+02, 1.55500649e+02, 1.56060000e+02])
```python
class MiyamotoNagaiP:
def __init__(self, dict_params):
self.amp = dict_params['amp']
self.a = dict_params['a']
self.b = dict_params['b']
def __str__(self):
return f"amp={self.amp}, a={self.a}, b={self.b}"
def __repr__(self):
return f"amp={self.amp}, a={self.a}, b={self.b}"
class MassScaleP:
def __init__(self, dict_params):
self.amp = dict_params['amp']
self.a = dict_params['a']
def __str__(self):
return f"amp={self.amp}, a={self.a}"
def __repr__(self):
return f"amp={self.amp}, a={self.a}"
```
```python
amp1 = widgets.FloatSlider(min=110000000.0*10**(-1), max=110000000.0*10**(1), step=110000000.0*0.1)
b1 = widgets.FloatSlider(min=0.495*(100-70)/100, max=0.495*(100+70)/100, step=0.495*0.1)
ui = widgets.HBox([amp1, b1])
def f(amp1, b1):
global bulge_potential
bulge_dict = {'amp': amp1, 'a':0, 'b': b1 }
bulge_potential = MiyamotoNagaiP(bulge_dict)
print((amp1, b1))
bulge_params = widgets.interactive_output(f, {'amp1': amp1, 'b1': b1})
display(ui, bulge_params)
```
HBox(children=(FloatSlider(value=11000000.0, max=1100000000.0, min=11000000.0, step=11000000.0), FloatSlider(v…
Output()
```python
amp2 = widgets.FloatSlider(min=3900000000.0*10**(-1), max=3900000000.0*10**(1), step=3900000000.0*0.1)
a2 = widgets.FloatSlider(min=5.3*(100-90)/100, max=5.3*(100+90)/100, step=5.3*0.1)
b2 = widgets.FloatSlider(min=0.25*(100-90)/100, max=0.25*(100+90)/100, step=0.25*0.1)
ui = widgets.HBox([amp2, a2, b2])
def f(amp2, a2, b2):
global thin_disk_potential
thin_disk_dict = {'amp': amp2, 'a':a2, 'b': b2 }
thin_disk_potential = MiyamotoNagaiP(thin_disk_dict)
print((amp2, a2, b2))
thin_disk_params = widgets.interactive_output(f, {'amp2': amp2, 'a2': a2, 'b2': b2})
display(ui, thin_disk_params)
```
HBox(children=(FloatSlider(value=390000000.0, max=39000000000.0, min=390000000.0, step=390000000.0), FloatSlid…
Output()
```python
amp3 = widgets.FloatSlider(min=39000000000.0*10**(-0.5), max=39000000000.0*10**(0.5), step=39000000000.0*0.1)
a3 = widgets.FloatSlider(min=2.6*(100-20)/100, max=2.6*(100+20)/100, step=2.6*0.1)
b3 = widgets.FloatSlider(min=0.8*(100-90)/100, max=0.8*(100+90)/100, step=0.8*0.1)
ui = widgets.HBox([amp3, a3, b3])
def f(amp3, a3, b3):
global thick_disk_potential
thick_disk_dict = {'amp': amp3, 'a':a3, 'b': b3 }
thick_disk_potential = MiyamotoNagaiP(thick_disk_dict)
print((amp3, a3, b3))
thick_disk_params = widgets.interactive_output(f, {'amp3': amp3, 'a3': a3, 'b3': b3})
display(ui, thin_disk_params)
```
HBox(children=(FloatSlider(value=12332882874.65668, max=123328828746.5668, min=12332882874.65668, step=3900000…
Output(outputs=({'output_type': 'stream', 'text': '(390000000.0, 0.53, 0.025)\n', 'name': 'stdout'},))
```python
amp4 = widgets.FloatSlider(min=500.0*10**(-0.5), max=500.0*10**(0.5), step=500.0*0.1)
h_r = widgets.FloatSlider(min=5.3*(100-90)/100, max=5.3*(100+90)/100, step=5.3*0.1)
ui = widgets.HBox([amp4, h_r ])
def f(amp4, h_r):
global exp_disk_potential
exp_disk_dict = {'amp': amp4, 'a': h_r}
exp_disk_potential = MassScaleP(exp_disk_dict)
print((amp4, h_r))
exp_disk_params = widgets.interactive_output(f, {'amp4': amp4, 'h_r': h_r})
display(ui, exp_disk_params)
```
HBox(children=(FloatSlider(value=158.11388300841898, max=1581.1388300841897, min=158.11388300841898, step=50.0…
Output()
```python
amp5 = widgets.FloatSlider(min=140000000000.0*10**(-1), max=140000000000.0*10**(1), step=140000000000.0*0.1)
a5 = widgets.FloatSlider(min=13*(100-90)/100, max=13*(100+90)/100, step=13*0.1)
ui = widgets.HBox([amp5, a5 ])
def f(amp5, a5):
global dark_halo_potential
dark_halo_dict = {'amp': amp5, 'a': a5}
dark_halo_potential = MassScaleP(dark_halo_dict)
print((amp5, a5))
dark_halo_params = widgets.interactive_output(f, {'amp5': amp5, 'a5': a5})
display(ui, dark_halo_params)
```
HBox(children=(FloatSlider(value=14000000000.0, max=1400000000000.0, min=14000000000.0, step=14000000000.0), F…
Output()
```python
amp6 = widgets.FloatSlider(min=8000000.0*10**(-1), max=8000000.0*10**(1), step=8000000.0*0.1)
a6 = widgets.FloatSlider(min=20*(100-90)/100, max=20*(100+90)/100, step=20*0.1)
ui = widgets.HBox([amp6, a6 ])
def f(amp6, a6):
global burkert_halo_potential
burkert_halo_dict = {'amp': amp6, 'a': a6}
burkert_halo_potential = MassScaleP(burkert_halo_dict)
print((amp6, a6))
burkert_halo_params = widgets.interactive_output(f, {'amp6': amp6, 'a6': a6})
display(ui, burkert_halo_params)
```
HBox(children=(FloatSlider(value=800000.0, max=80000000.0, min=800000.0, step=800000.0), FloatSlider(value=2.0…
Output()
```python
lista=np.linspace(0.001, 1.02*np.max(r_data), 10*len(r_data))
```
```python
bulge_potential, thin_disk_potential, thick_disk_potential, exp_disk_potential, dark_halo_potential, burkert_halo_potential
```
(amp=11000000.0, a=0, b=0.1485,
amp=390000000.0, a=0.53, b=0.025,
amp=12332882874.65668, a=2.08, b=0.08,
amp=158.11388300841898, a=0.53,
amp=14000000000.0, a=1.3,
amp=800000.0, a=2.0)
```python
data_rows = [('BULGE', 110000000.0, 1.0, 0.0, 20, 0.495, 70),
('THIN DISK', 3900000000.0, 1.0, 5.3, 90, 0.25, 1),
('THICK DISK', 39000000000.0, 0.5, 2.6, 20, 0.8, 1),
('EXP DISK', 500.0, 0.5, 5.3, 90, 0.0, 0),
('DARK HALO', 140000000000.0, 1.0, 13.0, 90, 0.0, 0),
('BURKERT HALO', 8000000.0, 1.0, 20.0, 90, 0.0, 0)]
input_params = Table.Table(rows=data_rows, names=('component', 'mass', 'threshold_mass', 'a (kpc)', 'threshold_a', 'b (kpc)', 'threshold_b'))
def input_component(component, guess_mass, guess_a, guess_b):
component_mass, component_scale_a, component_scale_b = guess_mass, guess_a, guess_b
print('Set the guess parameters for', component)
try:
component_mass = float(input('Mass (in M_sun):'))
except:
print('No valid Mass for', component, '. It will be taken the default mass:', component_mass, 'M_sun')
try:
component_scale_a = float(input('Radial Scale Length (in kpc):'))
except:
print('No valid Radial Scale Length for', component, '. It will be taken the default Radial Scale Lenght:', component_scale_a, 'kpc')
if component not in ['EXP DISK', 'DARK HALO', 'BURKERT HALO' ]:
try:
component_scale_b = float(input('Vertical Scale Length (in kpc):'))
except:
print('No valid Vertical Scale Length for', component, '. It will be taken the default Vertical Scale Lenght:', component_scale_b, 'kpc')
return component_mass, component_scale_a, component_scale_b
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
x_offset = 0.0 # It defines a radial coordinate offset as user input
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
# Real data:
r_data=tt['r']-x_offset # The txt file must contain the radial coordinate values in kpc
v_c_data=tt['vel'] # velocity in km/s
v_c_err_data = tt['e_vel'] # and velocity error in km/s
# This loop is needed since galpy fails when r=0 or very close to 0
for i in range(len(r_data)):
if r_data[i]<1e-3:
r_data[i]=1e-3
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Initial parameters:
c_bulge, amp1, delta_mass_bulge, a1, delta_radial_bulge, b1, delta_vertical_bulge = input_params[0]
amp1, a1, b1 = input_component(c_bulge, amp1, a1, b1)
#print(mass, radial, vertical)
c_tn, amp2, delta_mass_tn, a2, delta_radial_tn, b2, delta_vertical_tn = input_params[1]
amp2, a2, b2 = input_component(c_tn, amp2, a2, b2)
#print(mass, radial, vertical)
c_tk, amp3, delta_mass_tk, a3, delta_radial_tk, b3, delta_vertical_tk = input_params[2]
amp3, a3, b3 = input_component(c_tk, amp3, a3, b3)
#print(mass, radial, vertical)
c_ex, amp4, delta_mass_ex, h_r, delta_radial_ex, vertical_ex, delta_vertical_ex = input_params[3]
amp4, h_r, vertical_ex = input_component(c_ex, amp4, h_r, vertical_ex)
#print(mass, radial, vertical)
c_dh, amp5, delta_mass_dh, a5, delta_radial_dh, b5, delta_vertical_dh = input_params[4]
amp5, a5, b5 = input_component(c_dh, amp5, a5, b5)
#print(mass, radial, vertical)
c_bh, amp6, delta_mass_bh, a6, delta_radial_bh, b6, delta_vertical_bh = input_params[5]
amp6, a6, b6 = input_component(c_bh, amp6, a6, b6)
```
Set the guess parameters for BULGE
Mass (in M_sun):
No valid Mass for BULGE . It will be taken the default mass: 110000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for BULGE . It will be taken the default Radial Scale Lenght: 0.0 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for BULGE . It will be taken the default Vertical Scale Lenght: 0.495 kpc
Set the guess parameters for THIN DISK
Mass (in M_sun):
No valid Mass for THIN DISK . It will be taken the default mass: 3900000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for THIN DISK . It will be taken the default Radial Scale Lenght: 5.3 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for THIN DISK . It will be taken the default Vertical Scale Lenght: 0.25 kpc
Set the guess parameters for THICK DISK
Mass (in M_sun):
No valid Mass for THICK DISK . It will be taken the default mass: 39000000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for THICK DISK . It will be taken the default Radial Scale Lenght: 2.6 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for THICK DISK . It will be taken the default Vertical Scale Lenght: 0.8 kpc
Set the guess parameters for EXP DISK
Mass (in M_sun):
No valid Mass for EXP DISK . It will be taken the default mass: 500.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for EXP DISK . It will be taken the default Radial Scale Lenght: 5.3 kpc
Set the guess parameters for DARK HALO
Mass (in M_sun):
No valid Mass for DARK HALO . It will be taken the default mass: 140000000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for DARK HALO . It will be taken the default Radial Scale Lenght: 13.0 kpc
Set the guess parameters for BURKERT HALO
Mass (in M_sun):
No valid Mass for BURKERT HALO . It will be taken the default mass: 8000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for BURKERT HALO . It will be taken the default Radial Scale Lenght: 20.0 kpc
```python
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_potential.amp*units.Msun,
a=bulge_potential.a*units.kpc,
b=bulge_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=thin_disk_potential.amp*units.Msun,
a=thin_disk_potential.a*units.kpc,
b=thin_disk_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=thick_disk_potential.amp*units.Msun,
a=thick_disk_potential.a*units.kpc,
b=thick_disk_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
EX_Disk_p = RazorThinExponentialDiskPotential(amp=exp_disk_potential.amp*(units.Msun/(units.pc**2)),
hr=exp_disk_potential.a*units.kpc, maxiter=20, tol=0.001,
normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
NFW_p = NFWPotential(amp=dark_halo_potential.amp*units.Msun,
a=dark_halo_potential.a*units.kpc, normalize=False, ro=r_0, vo=v_0)
BK_p = BurkertPotential(amp=burkert_halo_potential.amp*units.Msun/(units.kpc)**3,
a=burkert_halo_potential.a*units.kpc, normalize=False, ro=r_0, vo=v_0)
# Circular velocities in km/s
MN_Bulge = calcRotcurve(MN_Bulge_p, lista, phi=None)*220
MN_Thin_Disk = calcRotcurve(MN_Thin_Disk_p, lista, phi=None)*220
MN_Thick_Disk = calcRotcurve(MN_Thick_Disk_p, lista, phi=None)*220
EX_Disk = calcRotcurve(EX_Disk_p, lista, phi=None)*220
NFW = calcRotcurve(NFW_p, lista, phi=None)*220
BK = calcRotcurve(BK_p, lista, phi=None)*220
# Circular velocity for the composition of 5 potentials in km/s
v_circ_comp = calcRotcurve([MN_Bulge_p,MN_Thin_Disk_p,MN_Thick_Disk_p, EX_Disk_p, NFW_p, BK_p], lista, phi=None)*220
```
```python
bulge_potential
```
amp=11000000.0, a=0, b=0.1485
```python
c_bulge, amp1, delta_mass_bulge, a1, delta_radial_bulge, b1, delta_vertical_bulge
```
('BULGE', 110000000.0, 1.0, 0.0, 20, 0.495, 70)
```python
c_dh, amp5, delta_mass_dh, a5, delta_radial_dh, b5, delta_vertical_dh
```
('DARK HALO', 140000000000.0, 1.0, 13.0, 90, 0.0, 0)
```python
from scipy.optimize import curve_fit
```
# Bulge_NFW_potentials
```python
def Bulge_NFW_potentials( r, delta_r, bulge_amp, bulge_a, bulge_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_amp*units.Msun,
a=bulge_a*units.kpc,
b=bulge_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([MN_Bulge_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp1/(10**delta_mass_bulge), a1, b1*(1-0.01*delta_vertical_bulge), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp1*(10**delta_mass_bulge), 0.1*delta_radial_bulge, b1*(1+0.01*delta_vertical_bulge), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
11000000.0,
0.0,
0.14849999999999997,
14000000000.0,
1.2999999999999998),
(10, 1100000000.0, 2.0, 0.8415, 1400000000000.0, 24.7))
```python
popt, pcov = curve_fit(Bulge_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp1, a1, b1, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, Bulge_NFW_potentials( r_data, *popt ) )
```
[-3.52351441e-01 1.73198523e+08 8.56968382e-01 5.44170287e-01
1.87733502e+11 1.09055832e+01] [3.00891852e-01 9.65470703e+08 2.41854286e+03 2.41447933e+03
3.41114598e+10 1.85516675e+00]
[<matplotlib.lines.Line2D at 0x1229b5c88>]
# Bulge_ThinDisk_NFW_potentials
```python
c_tn, amp2, delta_mass_tn, a2, delta_radial_tn, b2, delta_vertical_tn
```
('THIN DISK', 3900000000.0, 1.0, 5.3, 90, 0.25, 1)
```python
def Bulge_ThinDisk_NFW_potentials( r, delta_r, bulge_amp, bulge_a, bulge_b, tn_amp, tn_a, tn_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_amp*units.Msun,
a=bulge_a*units.kpc,
b=bulge_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=tn_amp*units.Msun,
a=tn_a*units.kpc,
b=tn_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([MN_Bulge_p, MN_Thin_Disk_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp1/(10**delta_mass_bulge), a1, b1*(1-0.01*delta_vertical_bulge), amp2/(10**delta_mass_tn), a2*(1-0.01*delta_radial_tn), b2/(10**delta_vertical_tn), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp1*(10**delta_mass_bulge), 0.1*delta_radial_bulge, b1*(1+0.01*delta_vertical_bulge), amp2*(10**delta_mass_tn), a2*(1+0.01*delta_radial_tn), b2*(10**delta_vertical_tn), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
11000000.0,
0.0,
0.14849999999999997,
390000000.0,
0.5299999999999999,
0.025,
14000000000.0,
1.2999999999999998),
(10,
1100000000.0,
2.0,
0.8415,
39000000000.0,
10.069999999999999,
2.5,
1400000000000.0,
24.7))
```python
popt, pcov = curve_fit(Bulge_ThinDisk_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp1, a1, b1, amp2, a2, b2, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, Bulge_ThinDisk_NFW_potentials( r_data, *popt ) )
```
[-2.78902447e-01 1.24430536e+07 1.46675591e+00 7.61836853e-01
7.13107384e+09 2.68408162e+00 1.71493848e-01 2.57425690e+11
1.62738596e+01] [2.38046289e-01 2.88592063e+11 7.18582305e+05 7.18997942e+05
2.81004025e+11 8.08245548e+05 8.08244419e+05 1.49677219e+11
9.47114773e+00]
[<matplotlib.lines.Line2D at 0x120e158d0>]
```python
```
# ThinDisk_NFW_potentials
```python
def ThinDisk_NFW_potentials( r, delta_r, tn_amp, tn_a, tn_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=tn_amp*units.Msun,
a=tn_a*units.kpc,
b=tn_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([ MN_Thin_Disk_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp2/(10**delta_mass_tn), a2*(1-0.01*delta_radial_tn), b2/(10**delta_vertical_tn), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp2*(10**delta_mass_tn), a2*(1+0.01*delta_radial_tn), b2*(10**delta_vertical_tn), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
390000000.0,
0.5299999999999999,
0.025,
14000000000.0,
1.2999999999999998),
(10, 39000000000.0, 10.069999999999999, 2.5, 1400000000000.0, 24.7))
```python
popt, pcov = curve_fit(ThinDisk_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp2, a2, b2, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, ThinDisk_NFW_potentials( r_data, *popt ) )
```
[-2.78025944e-01 7.11898506e+09 2.74051174e+00 1.07097147e-01
2.57638242e+11 1.62771507e+01] [1.08072512e-01 2.90526942e+09 6.63797844e+05 6.63797792e+05
7.57432197e+10 4.37991102e+00]
[<matplotlib.lines.Line2D at 0x120d19e80>]
```python
```
```python
True and False
```
False
```python
run args_input.py a b
```
3
['args_input.py', 'a', 'b']
('a', 'is delicious. Would you like to try some?\n')
Or would you rather have the b ?
```python
args = [1, 2, 3]
flag = True
for i in args:
if i not in [1, 2, 4, 5]:
flag = False
```
```python
flag
```
False
```python
```
```python
fig = plt.figure(1)
ax = fig.add_axes((0.41, 0.1, 0.55, 0.85))
#ax.yaxis.set_ticks_position('both')
#ax.tick_params(axis='y', which='both', labelleft=True, labelright=True)
# Data
CV_galaxy = ax.errorbar(r_data, v_c_data, v_c_err_data, c='k', fmt='', ls='none')
CV_galaxy_dot = ax.scatter(r_data, v_c_data, c='k')
# A plot for each rotation curve with the colors indicated below
MN_b_plot, = ax.plot(lista, MN_Bulge, linestyle='--', c='gray')
MN_td_plot, = ax.plot(lista, MN_Thin_Disk, linestyle='--', c='purple')
MN_tkd_plot, = ax.plot(lista, MN_Thick_Disk, linestyle='--', c='blue')
EX_d_plot, = ax.plot(lista, EX_Disk, linestyle='--', c='cyan')
NFW_plot, = ax.plot(lista, NFW, linestyle='--', c='green')
BK_plot, = ax.plot(lista, BK, linestyle='--', c='orange')
# Composed rotation curve
v_circ_comp_plot, = ax.plot(lista, v_circ_comp, c='k')
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
```
```python
rax = plt.axes((0.07, 0.8, 0.21, 0.15))
check = CheckButtons(rax, ('MN Bulge (GRAY)', 'MN Thin Disc (PURPLE)', 'MN Thick Disc (BLUE)', 'Exp. Disc (CYAN)', 'NFW - Halo (GREEN)', 'Burkert - Halo (ORANGE)'), (True, True, True, True, True, True))
for r in check.rectangles: # Checkbox options-colors
r.set_facecolor("lavender")
r.set_edgecolor("black")
#r.set_alpha(0.2)
[ll.set_color("black") for l in check.lines for ll in l]
[ll.set_linewidth(2) for l in check.lines for ll in l]
```
[None, None, None, None, None, None, None, None, None, None, None, None]
```python
MN_b_amp_ax = fig.add_axes((0.09,0.75,0.17,0.03))
MN_b_amp_s = Slider(MN_b_amp_ax, r"$M$($M_\odot$)", input_params['mass'][0]/(10**input_params['threshold_mass'][0]), input_params['mass'][0]*(10**input_params['threshold_mass'][0]), valinit=input_params['mass'][0], color='gray', valfmt='%1.3E')
MN_b_a_ax = fig.add_axes((0.09,0.72,0.17,0.03))
MN_b_a_s = Slider(MN_b_a_ax, "$a$ ($kpc$)", 0, 0.1*input_params['threshold_a'][0], valinit=input_params['a (kpc)'][0], color='gray')
MN_b_b_ax = fig.add_axes((0.09,0.69,0.17,0.03))
MN_b_b_s = Slider(MN_b_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][0]*(1-0.01*input_params['threshold_b'][0]), input_params['b (kpc)'][0]*(1+0.01*input_params['threshold_b'][0]), valinit=input_params['b (kpc)'][0], color='gray')
# Thin disk - purple
MN_td_amp_ax = fig.add_axes((0.09,0.63,0.17,0.03))
MN_td_amp_s = Slider(MN_td_amp_ax, r"$M$($M_\odot$)", input_params['mass'][1]/(10**input_params['threshold_mass'][1]), input_params['mass'][1]*(10**input_params['threshold_mass'][1]), valinit=input_params['mass'][1], color='purple', valfmt='%1.3E')
MN_td_a_ax = fig.add_axes((0.09,0.60,0.17,0.03))
MN_td_a_s = Slider(MN_td_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][1]*(1-0.01*input_params['threshold_a'][1]), input_params['a (kpc)'][1]*(1+0.01*input_params['threshold_a'][1]), valinit=input_params['a (kpc)'][1], color='purple')
MN_td_b_ax = fig.add_axes((0.09,0.57,0.17,0.03))
MN_td_b_s = Slider(MN_td_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][1]/(10**input_params['threshold_b'][1]), input_params['b (kpc)'][1]*(10**input_params['threshold_b'][1]), valinit=input_params['b (kpc)'][1], color='purple')
# Thick disk - Blue
MN_tkd_amp_ax = fig.add_axes((0.09,0.51,0.17,0.03))
MN_tkd_amp_s = Slider(MN_tkd_amp_ax, r"$M$($M_\odot$)", input_params['mass'][2]/(10**input_params['threshold_mass'][2]), input_params['mass'][2]*(10**input_params['threshold_mass'][2]), valinit=input_params['mass'][2], color='blue', valfmt='%1.3E')
MN_tkd_a_ax = fig.add_axes((0.09,0.48,0.17,0.03))
MN_tkd_a_s = Slider(MN_tkd_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][2]*(1-0.01*input_params['threshold_a'][2]), input_params['a (kpc)'][2]*(1+0.01*input_params['threshold_a'][2]), valinit=input_params['a (kpc)'][2], color='blue')
MN_tkd_b_ax = fig.add_axes((0.09,0.45,0.17,0.03))
MN_tkd_b_s = Slider(MN_tkd_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][2]/(10**input_params['threshold_b'][2]), input_params['b (kpc)'][2]*(10**input_params['threshold_b'][2]), valinit=input_params['b (kpc)'][2], color='blue')
# Exponential disk - Cyan
MN_ed_amp_ax = fig.add_axes((0.09,0.39,0.17,0.03))
MN_ed_amp_s = Slider(MN_ed_amp_ax, r"$\Sigma_0$($M_\odot/pc^2$)", input_params['mass'][3]/(10**input_params['threshold_mass'][3]), input_params['mass'][3]*(10**input_params['threshold_mass'][3]), valinit=input_params['mass'][3], color='cyan', valfmt='%1.3E')
MN_ed_a_ax = fig.add_axes((0.09,0.36,0.17,0.03))
MN_ed_a_s = Slider(MN_ed_a_ax, "$h_r$ ($kpc$)", input_params['a (kpc)'][3]*(1-0.01*input_params['threshold_a'][3]), input_params['a (kpc)'][3]*(1+0.01*input_params['threshold_a'][3]), valinit=input_params['a (kpc)'][3], color='cyan')
# NFW Halo - green
NFW_amp_ax = fig.add_axes((0.09,0.30,0.17,0.03))
NFW_amp_s = Slider(NFW_amp_ax, r"$M_0$($M_\odot$)", input_params['mass'][4]/(10*input_params['threshold_mass'][4]), input_params['mass'][4]*(10**input_params['threshold_mass'][4]), valinit=input_params['mass'][4], color='green', valfmt='%1.3E')
NFW_a_ax = fig.add_axes((0.09,0.27,0.17,0.03))
NFW_a_s = Slider(NFW_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][4]*(1-0.01*input_params['threshold_a'][4]), input_params['a (kpc)'][4]*(1+0.01*input_params['threshold_a'][4]), valinit=input_params['a (kpc)'][4], color='green')
# Burkert Halo - orange
BK_amp_ax = fig.add_axes((0.09,0.21,0.17,0.03))
BK_amp_s = Slider(BK_amp_ax, r"$\rho_0$($M_\odot/kpc^3$)", input_params['mass'][5]/(10*input_params['threshold_mass'][5]), input_params['mass'][5]*(10**input_params['threshold_mass'][5]), valinit=input_params['mass'][5], color='orange', valfmt='%1.3E')
BK_a_ax = fig.add_axes((0.09,0.18,0.17,0.03))
BK_a_s = Slider(BK_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][5]*(1-0.01*input_params['threshold_a'][5]), input_params['a (kpc)'][5]*(1+0.01*input_params['threshold_a'][5]), valinit=input_params['a (kpc)'][5], color='orange')
```
```python
# Bulge
def MN_b_amp_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
amp1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=val*units.Msun,a=a1*units.kpc,b=b1*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_b_a_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
a1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=amp1*units.Msun,a=val*units.kpc,b=b1*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_b_b_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
b1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=amp1*units.Msun,a=a1*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Thin disk
def MN_td_amp_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
amp2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=val*units.Msun,a=a2*units.kpc,b=b2*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_td_a_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
a2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=amp2*units.Msun,a=val*units.kpc,b=b2*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_td_b_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
b2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=amp2*units.Msun,a=a2*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Thick disk
def MN_tkd_amp_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
amp3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=val*units.Msun,a=a3*units.kpc,b=b3*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_tkd_a_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
a3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=amp3*units.Msun,a=val*units.kpc,b=b3*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_tkd_b_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
b3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=amp3*units.Msun,a=a3*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Exponential disk
def MN_ed_amp_s_func(val):
if EX_d_plot.get_visible() == True:
global EX_Disk_p, amp4,h_r
amp4=val*1
EX_Disk_p = RazorThinExponentialDiskPotential(amp=val*(units.Msun/(units.pc**2)), hr=h_r*units.kpc, maxiter=20, tol=0.001, normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
update_rot_curve()
def MN_ed_a_s_func(val):
if EX_d_plot.get_visible() == True:
global EX_Disk_p, amp4,h_r
h_r=val*1
EX_Disk_p = RazorThinExponentialDiskPotential(amp=amp4*(units.Msun/(units.pc**2)), hr=val*units.kpc, maxiter=20, tol=0.001, normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
update_rot_curve()
# NFW Halo
def NFW_amp_s_func(val):
if NFW_plot.get_visible() == True:
global NFW_p, amp5,a5
amp5=val*1
NFW_p = NFWPotential(amp=val*units.Msun, a=a5*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
def NFW_a_s_func(val):
if NFW_plot.get_visible() == True:
global NFW_p, amp5,a5
a5=val*1
NFW_p = NFWPotential(amp=amp5*units.Msun, a=val*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
# Burkert Halo
def BK_amp_s_func(val):
if BK_plot.get_visible() == True:
global BK_p, amp6,a6
amp6=val*1
BK_p = BurkertPotential(amp=val*units.Msun/(units.kpc)**3, a=a6*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
def BK_a_s_func(val):
if BK_plot.get_visible() == True:
global BK_p, amp6,a6
a6=val*1
BK_p = BurkertPotential(amp=amp6*units.Msun/(units.kpc)**3, a=val*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
```
```python
def update_rot_curve():
ax.clear()
global MN_b_plot, MN_Bulge_p, MN_Thin_Disk_p,MN_Thick_Disk_p, MN_td_plot,MN_tkd_plot, NFW_p, NFW_plot, EX_d_plot, EX_Disk_p, CV_galaxy, CV_galaxy_dot, BK_p, BK_plot
composite_pot_array=[]
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
#ax.xaxis.set_major_locator(ticker.MultipleLocator(5))
ax.set_xlim([0, 1.02*r_data[-1]])
ax.set_ylim([0,np.max(v_c_data)*1.2])
if MN_b_plot.get_visible() == True:
MN_Bulge = calcRotcurve(MN_Bulge_p, lista, phi=None)*220
MN_b_plot, = ax.plot(lista, MN_Bulge, linestyle='--', c='gray')
composite_pot_array.append(MN_Bulge_p)
if MN_td_plot.get_visible() == True:
MN_Thin_Disk = calcRotcurve(MN_Thin_Disk_p, lista, phi=None)*220
MN_td_plot, = ax.plot(lista, MN_Thin_Disk, linestyle='--', c='purple')
composite_pot_array.append(MN_Thin_Disk_p)
if MN_tkd_plot.get_visible() == True:
MN_Thick_Disk = calcRotcurve(MN_Thick_Disk_p, lista, phi=None)*220
MN_tkd_plot, = ax.plot(lista, MN_Thick_Disk, linestyle='--', c='blue')
composite_pot_array.append(MN_Thick_Disk_p)
if NFW_plot.get_visible() == True:
NFW = calcRotcurve(NFW_p, lista, phi=None)*220
NFW_plot, = ax.plot(lista, NFW, linestyle='--', c='green')
composite_pot_array.append(NFW_p)
if EX_d_plot.get_visible() == True:
EX_Disk = calcRotcurve(EX_Disk_p, lista, phi=None)*220
EX_d_plot, = ax.plot(lista, EX_Disk, linestyle='--', c='cyan')
composite_pot_array.append(EX_Disk_p)
if BK_plot.get_visible() == True:
BK = calcRotcurve(BK_p, lista, phi=None)*220
BK_plot, = ax.plot(lista, BK, linestyle='--', c='orange')
composite_pot_array.append(BK_p)
CV_galaxy = ax.errorbar(r_data, v_c_data, v_c_err_data, c='k', fmt='', ls='none')
CV_galaxy_dot = ax.scatter(r_data, v_c_data, c='k')
v_circ_comp = calcRotcurve(composite_pot_array, lista, phi=None)*220
v_circ_comp_plot, = ax.plot(lista, v_circ_comp, c='k')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we define the sliders update functions
MN_b_amp_s.on_changed(MN_b_amp_s_func)
MN_b_a_s.on_changed(MN_b_a_s_func)
MN_b_b_s.on_changed(MN_b_b_s_func)
MN_td_amp_s.on_changed(MN_td_amp_s_func)
MN_td_a_s.on_changed(MN_td_a_s_func)
MN_td_b_s.on_changed(MN_td_b_s_func)
MN_tkd_amp_s.on_changed(MN_tkd_amp_s_func)
MN_tkd_a_s.on_changed(MN_tkd_a_s_func)
MN_tkd_b_s.on_changed(MN_tkd_b_s_func)
NFW_amp_s.on_changed(NFW_amp_s_func)
NFW_a_s.on_changed(NFW_a_s_func)
BK_amp_s.on_changed(BK_amp_s_func)
BK_a_s.on_changed(BK_a_s_func)
MN_ed_amp_s.on_changed(MN_ed_amp_s_func)
MN_ed_a_s.on_changed(MN_ed_a_s_func)
```
0
```python
def reset(event):
MN_b_amp_s.reset()
MN_b_a_s.reset()
MN_b_b_s.reset()
MN_td_amp_s.reset()
MN_td_a_s.reset()
MN_td_b_s.reset()
MN_tkd_amp_s.reset()
MN_tkd_a_s.reset()
MN_tkd_b_s.reset()
MN_ed_amp_s.reset()
MN_ed_a_s.reset()
NFW_amp_s.reset()
NFW_a_s.reset()
BK_amp_s.reset()
BK_a_s.reset()
axcolor="lavender"
resetax = fig.add_axes((0.07, 0.08, 0.08, 0.05))
button_reset = Button(resetax, 'Reset', color=axcolor)
button_reset.on_clicked(reset)
```
0
```python
def check_on_clicked(label):
if label == 'MN Bulge (GRAY)':
MN_b_plot.set_visible(not MN_b_plot.get_visible())
update_rot_curve()
elif label == 'MN Thin Disc (PURPLE)':
MN_td_plot.set_visible(not MN_td_plot.get_visible())
update_rot_curve()
elif label == 'MN Thick Disc (BLUE)':
MN_tkd_plot.set_visible(not MN_tkd_plot.get_visible())
update_rot_curve()
elif label == 'Exp. Disc (CYAN)':
EX_d_plot.set_visible(not EX_d_plot.get_visible())
update_rot_curve()
elif label == 'NFW - Halo (GREEN)':
NFW_plot.set_visible(not NFW_plot.get_visible())
update_rot_curve()
elif label == 'Burkert - Halo (ORANGE)':
BK_plot.set_visible(not BK_plot.get_visible())
update_rot_curve()
plt.draw()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Plotting all the curves
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
#ax.xaxis.set_major_locator(ticker.MultipleLocator(5))
#ax.set_xlim([0, np.max(lista)])
#ax.set_ylim([0,np.max(v_c_data)*1.2])
check.on_clicked(check_on_clicked)
```
0
```python
ax
```
<matplotlib.axes._axes.Axes at 0x120acb748>
```python
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons, TextBox # Matplotlib widgets
```
```python
CheckButtons?
```
```python
%matplotlib
t = np.arange(0.0, 2.0, 0.01)
s0 = np.sin(2*np.pi*t)
s1 = np.sin(4*np.pi*t)
s2 = np.sin(6*np.pi*t)
fig, ax = plt.subplots()
l0, = ax.plot(t, s0, visible=False, lw=2, color='k', label='2 Hz')
l1, = ax.plot(t, s1, lw=2, color='r', label='4 Hz')
l2, = ax.plot(t, s2, lw=2, color='g', label='6 Hz')
plt.subplots_adjust(left=0.2)
lines = [l0, l1, l2]
# Make checkbuttons with all plotted lines with correct visibility
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
labels = [str(line.get_label()) for line in lines]
visibility = [line.get_visible() for line in lines]
check = CheckButtons(rax, labels, visibility)
def func(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(func)
```
Using matplotlib backend: MacOSX
0
```python
visibility
```
[False, True, True]
```python
check.get_status()
```
[True, False, False]
```python
l1.set_visible?
```
```python
print( check.get_status() )
check_visibility = check.get_status()
MN_b_plot.set_visible(check_visibility[0])
MN_td_plot.set_visible(check_visibility[1])
MN_tkd_plot.set_visible(check_visibility[2])
EX_d_plot.set_visible(check_visibility[3])
NFW_plot.set_visible(check_visibility[4])
BK_plot.set_visible(check_visibility[5])
```
|
andresGranadosCREPO_NAMEGalRotpyPATH_START.@GalRotpy_extracted@GalRotpy-master@notebook@GalRotpy.ipynb@.PATH_END.py
|
{
"filename": "pairsubtraction_demo.py",
"repo_name": "kpicteam/kpic_pipeline",
"repo_path": "kpic_pipeline_extracted/kpic_pipeline-main/examples/pairsubtraction_demo.py",
"type": "Python"
}
|
import os
import numpy as np
import astropy.io.fits as fits
import kpicdrp.data as data
from kpicdrp.caldb import det_caldb
import kpicdrp.extraction as extraction
from glob import glob
try:
import mkl
mkl.set_num_threads(1)
except:
pass
raw_folder = "/scr3/kpic/Data/210425/spec/"
out_folder = "/scr3/jruffio/data/kpic/20210425_LSRJ1835+3259/raw_pairsub/"
if not os.path.exists(out_folder):
os.makedirs(out_folder)
# filenums_fib2 = [321,323,325,326,327,328,333,335,336,339]
# filenums_fib3 = [322,324,329,330,331,332,334,337,338,340]
init_num, Nim, Nit = 84,1,10
goal_fibers = []
currit = init_num
for k in range(Nit):
for l in range(Nim):
goal_fibers += [2, ]
for l in range(Nim):
goal_fibers += [3, ]
print(goal_fibers)
# exit()
template_fname = "nspec210425_{0:04d}.fits"
filenums = range(init_num, init_num + (Nim * Nit * 2))
filelist = [os.path.join(raw_folder, template_fname.format(i)) for i in filenums]
raw_sci_dataset = data.Dataset(filelist=filelist, dtype=data.DetectorFrame)
# fetch calibration files
badpixmap = det_caldb.get_calib(raw_sci_dataset[0], type="BadPixelMap")
sci_dataset = extraction.process_sci_raw2d(raw_sci_dataset, None, badpixmap, detect_cosmics=True, add_baryrv=True, nod_subtraction='pair', fiber_goals=goal_fibers)
sci_dataset.save(filedir=out_folder)
|
kpicteamREPO_NAMEkpic_pipelinePATH_START.@kpic_pipeline_extracted@kpic_pipeline-main@examples@pairsubtraction_demo.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/library/python/codecs/__init__.py",
"type": "Python"
}
|
from __codecs import loads, dumps, list_all_codecs, get_codec_id # noqa
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@library@python@codecs@__init__.py@.PATH_END.py
|
{
"filename": "zoom.py",
"repo_name": "vaexio/vaex",
"repo_path": "vaex_extracted/vaex-master/packages/vaex-ui/vaex/ui/plugin/zoom.py",
"type": "Python"
}
|
import functools
import matplotlib.widgets
import vaex.ui.plugin
from vaex.ui import undo
from vaex.ui.qt import *
from vaex.ui.icons import iconfile
import logging
import vaex.ui.undo as undo
logger = logging.getLogger("plugin.zoom")
@vaex.ui.plugin.pluginclass
class ZoomPlugin(vaex.ui.plugin.PluginPlot):
name = "zoom"
def __init__(self, dialog):
super(ZoomPlugin, self).__init__(dialog)
dialog.plug_toolbar(self.plug_toolbar, 1.2)
def plug_toolbar(self):
logger.info("adding zoom plugin")
self.dialog.menu_mode.addSeparator()
self.action_zoom_rect = QtGui.QAction(QtGui.QIcon(iconfile('zoom')), '&Zoom to rect', self.dialog)
self.action_zoom_rect.setShortcut("Ctrl+Alt+Z")
self.dialog.menu_mode.addAction(self.action_zoom_rect)
self.action_zoom_x = QtGui.QAction(QtGui.QIcon(iconfile('zoom_x')), '&Zoom x', self.dialog)
self.action_zoom_y = QtGui.QAction(QtGui.QIcon(iconfile('zoom_y')), '&Zoom y', self.dialog)
self.action_zoom = QtGui.QAction(QtGui.QIcon(iconfile('zoom')), '&Zoom(you should not read this)', self.dialog)
self.action_zoom_x.setShortcut("Ctrl+Alt+X")
self.action_zoom_y.setShortcut("Ctrl+Alt+Y")
self.dialog.menu_mode.addAction(self.action_zoom_x)
self.dialog.menu_mode.addAction(self.action_zoom_y)
self.dialog.menu_mode.addSeparator()
self.action_zoom_out = QtGui.QAction(QtGui.QIcon(iconfile('zoom_out')), '&Zoom out', self.dialog)
self.action_zoom_in = QtGui.QAction(QtGui.QIcon(iconfile('zoom_in')), '&Zoom in', self.dialog)
self.action_zoom_fit = QtGui.QAction(QtGui.QIcon(iconfile('arrow_out')), '&Reset view', self.dialog)
#self.action_zoom_use = QtGui.QAction(QtGui.QIcon(iconfile('chart_bar')), '&Use zoom area', self.dialog)
self.action_zoom_out.setShortcut("Ctrl+Alt+-")
self.action_zoom_in.setShortcut("Ctrl+Alt++")
self.action_zoom_fit.setShortcut("Ctrl+Alt+0")
self.dialog.menu_mode.addAction(self.action_zoom_out)
self.dialog.menu_mode.addAction(self.action_zoom_in)
self.dialog.menu_mode.addAction(self.action_zoom_fit)
self.dialog.action_group_main.addAction(self.action_zoom_rect)
self.dialog.action_group_main.addAction(self.action_zoom_x)
self.dialog.action_group_main.addAction(self.action_zoom_y)
#self.dialog.toolbar.addAction(self.action_zoom_out)
#self.dialog.add_shortcut(self.action_zoom_in,"+")
#self.dialog.add_shortcut(self.action_zoom_out,"-")
#self.dialog.add_shortcut(self.action_zoom_rect,"Z")
#self.dialog.add_shortcut(self.action_zoom_x,"Alt+X")
#self.dialog.add_shortcut(self.action_zoom_y,"Alt+Y")
#self.dialog.add_shortcut(self.action_zoom_fit, "0")
self.dialog.toolbar.addAction(self.action_zoom)
self.zoom_menu = QtGui.QMenu()
self.action_zoom.setMenu(self.zoom_menu)
self.zoom_menu.addAction(self.action_zoom_rect)
self.zoom_menu.addAction(self.action_zoom_x)
self.zoom_menu.addAction(self.action_zoom_y)
if self.dialog.dimensions == 1:
self.lastActionZoom = self.action_zoom_x # this makes more sense for histograms as default
else:
self.lastActionZoom = self.action_zoom_rect
self.dialog.toolbar.addSeparator()
#self.dialog.toolbar.addAction(self.action_zoom_out)
self.dialog.toolbar.addAction(self.action_zoom_fit)
self.action_zoom.triggered.connect(self.onActionZoom)
self.action_zoom_out.triggered.connect(self.onZoomOut)
self.action_zoom_in.triggered.connect(self.onZoomIn)
self.action_zoom_fit.triggered.connect(self.onZoomFit)
#self.action_zoom_use.triggered.connect(self.onZoomUse)
self.action_zoom.setCheckable(True)
self.action_zoom_rect.setCheckable(True)
self.action_zoom_x.setCheckable(True)
self.action_zoom_y.setCheckable(True)
def setMode(self, action):
useblit = True
axes_list = self.dialog.getAxesList()
if action == self.action_zoom_x:
print("zoom x")
self.lastActionZoom = self.action_zoom_x
self.dialog.currentModes = [matplotlib.widgets.SpanSelector(axes, functools.partial(self.onZoomX, axes=axes), 'horizontal', useblit=useblit) for axes in axes_list] #, rectprops={"color":"blue"})
if useblit:
self.dialog.canvas.draw() # buggy otherwise
if action == self.action_zoom_y:
self.lastActionZoom = self.action_zoom_y
self.dialog.currentModes = [matplotlib.widgets.SpanSelector(axes, functools.partial(self.onZoomY, axes=axes), 'vertical', useblit=useblit) for axes in axes_list] #, rectprops={"color":"blue"})
if useblit:
self.dialog.canvas.draw() # buggy otherwise
if action == self.action_zoom_rect:
print("zoom rect")
self.lastActionZoom = self.action_zoom_rect
self.dialog.currentModes = [matplotlib.widgets.RectangleSelector(axes, functools.partial(self.onZoomRect, axes=axes), useblit=useblit) for axes in axes_list] #, rectprops={"color":"blue"})
if useblit:
self.dialog.canvas.draw() # buggy otherwise
def onZoomIn(self, *args):
axes = self.getAxesList()[0] # TODO: handle propery multiple axes
self.dialog.zoom(0.5, axes)
self.dialog.queue_history_change("zoom in")
def onZoomOut(self):
axes = self.dialog.getAxesList()[0] # TODO: handle propery multiple axes
self.dialog.zoom(2., axes)
self.dialog.queue_history_change("zoom out")
def onActionZoom(self):
print("onactionzoom")
self.lastActionZoom.setChecked(True)
self.dialog.setMode(self.lastActionZoom)
self.syncToolbar()
def onZoomFit(self, *args):
#for i in range(self.dimensions):
# self.dialog.ranges[i] = None
# self.dialog.state.ranges_viewport[i] = None
# self.range_level = None
if 0:
for axisIndex in range(self.dimensions):
linkButton = self.linkButtons[axisIndex]
link = linkButton.link
if link:
logger.debug("sending link messages")
link.sendRanges(self.dialog.ranges[axisIndex], linkButton)
link.sendRangesShow(self.dialog.state.ranges_viewport[axisIndex], linkButton)
action = undo.ActionZoom(self.dialog.undoManager, "zoom to fit", self.dialog.set_ranges,
list(range(self.dialog.dimensions)),
self.dialog.state.ranges_viewport, self.dialog.state.range_level_show,
list(range(self.dialog.dimensions)),
ranges_viewport=[None] * self.dialog.dimensions, range_level_show=None)
#for layer in dialog.layers:
# layer.range_level = None # reset these... is this the right place?
action.do()
self.dialog.checkUndoRedo()
self.dialog.queue_history_change("zoom to fit")
if 0:
linked_buttons = [button for button in self.linkButtons if button.link is not None]
links = [button.link for button in linked_buttons]
if len(linked_buttons) > 0:
logger.debug("sending compute message")
vaex.dataset.Link.sendCompute(links, linked_buttons)
#linked_buttons[0].sendCompute(blacklist)
#if linkButtonLast: # only send once
# link = linkButtonLast.link
# logger.debug("sending compute message")
# link.sendCompute(linkButton)
#self.compute()
#self.dataset.executor.execute()
def onZoomUse(self, *args):
# TODO: when this will be an option again, implement this as action
# TODO: will we ever use this again? auto updates are much better
for i in range(self.dimensions):
self.dialog.ranges[i] = self.dialog.state.ranges_viewport[i]
self.range_level = None
for axisIndex in range(self.dimensions):
linkButton = self.linkButtons[axisIndex]
link = linkButton.link
if link:
logger.debug("sending link messages")
link.sendRanges(self.dialog.ranges[axisIndex], linkButton)
#link.sendRangesShow(self.dialog.state.ranges_viewport[axisIndex], linkButton)
linked_buttons = [button for button in self.linkButtons if button.link is not None]
links = [button.link for button in linked_buttons]
if len(linked_buttons) > 0:
logger.debug("sending compute message")
vaex.dataset.Link.sendCompute(links, linked_buttons)
self.compute()
self.dataset.executor.execute()
def onZoomX(self, xmin, xmax, axes):
axisIndex = axes.xaxis_index
#self.dialog.state.ranges_viewport[axisIndex] = xmin, xmax
# move the link code to the set ranges
if 0:
linkButton = self.linkButtons[axisIndex]
link = linkButton.link
if link:
logger.debug("sending link messages")
link.sendRangesShow(self.dialog.state.ranges_viewport[axisIndex], linkButton)
link.sendPlot(linkButton)
action = undo.ActionZoom(self.dialog.undoManager, "zoom x [%f,%f]" % (xmin, xmax),
self.dialog.set_ranges, list(range(self.dialog.dimensions)),
self.dialog.state.ranges_viewport,self.dialog.state.range_level_show, [axisIndex], ranges_viewport=[[xmin, xmax]])
action.do()
self.dialog.checkUndoRedo()
self.dialog.queue_history_change("zoom x")
def onZoomY(self, ymin, ymax, axes):
if len(self.dialog.state.ranges_viewport) == 1: # if 1d, y refers to range_level
#self.range_level = ymin, ymax
action = undo.ActionZoom(self.dialog.undoManager, "change level [%f,%f]" % (ymin, ymax), self.dialog.set_ranges, list(range(self.dialog.dimensions)),
self.dialog.state.ranges_viewport, self.dialog.state.range_level_show, [], range_level_show=[ymin, ymax])
else:
#self.dialog.state.ranges_viewport[axes.yaxis_index] = ymin, ymax
action = undo.ActionZoom(self.dialog.undoManager, "zoom y [%f,%f]" % (ymin, ymax), self.dialog.set_ranges, list(range(self.dialog.dimensions)),
self.dialog.state.ranges_viewport, self.dialog.state.range_level_show, [axes.yaxis_index], ranges_viewport=[[ymin, ymax]])
action.do()
self.dialog.checkUndoRedo()
self.dialog.queue_history_change("zoom y")
def onZoomRect(self, eclick, erelease, axes):
x1, y1 = (eclick.xdata, eclick.ydata)
x2, y2 = (erelease.xdata, erelease.ydata)
x = [x1, x2]
y = [y1, y2]
range_level = None
ranges_show = []
ranges = []
axis_indices = []
xmin_show, xmax_show = min(x), max(x)
ymin_show, ymax_show = min(y), max(y)
if self.dialog.state.ranges_viewport[0][0] > self.dialog.state.ranges_viewport[0][1]:
xmin_show, xmax_show = xmax_show, xmin_show
if len(self.dialog.state.ranges_viewport) == 1 and self.dialog.state.range_level_show[0] > self.dialog.state.range_level_show[1]:
ymin_show, ymax_show = ymax_show, ymin_show
elif self.dialog.state.ranges_viewport[1][0] > self.dialog.state.ranges_viewport[1][1]:
ymin_show, ymax_show = ymax_show, ymin_show
#self.dialog.state.ranges_viewport[axes.xaxis_index] = xmin_show, xmax_show
axis_indices.append(axes.xaxis_index)
ranges_show.append([xmin_show, xmax_show])
if len(self.dialog.state.ranges_viewport) == 1: # if 1d, y refers to range_level
#self.range_level = ymin_show, ymax_show
range_level = ymin_show, ymax_show
logger.debug("range refers to level: %r" % (self.dialog.state.range_level_show,))
else:
#self.dialog.state.ranges_viewport[axes.yaxis_index] = ymin_show, ymax_show
axis_indices.append(axes.yaxis_index)
ranges_show.append([ymin_show, ymax_show])
def delayed_zoom():
action = undo.ActionZoom(self.dialog.undoManager, "zoom to rect", self.dialog.set_ranges, list(range(self.dialog.dimensions)),
self.dialog.state.ranges_viewport,
self.dialog.state.range_level_show, axis_indices, ranges_viewport=ranges_show, range_level_show=range_level)
action.do()
self.dialog.checkUndoRedo()
#self.dialog.queue_update(delayed_zoom, delay=300)
delayed_zoom()
self.dialog.queue_history_change("zoom to rectangle")
if 1:
#self.dialog.state.ranges_viewport = list(ranges_show)
self.dialog.state.ranges_viewport[axes.xaxis_index] = list(ranges_show[0])
if self.dialog.dimensions == 2:
self.dialog.state.ranges_viewport[axes.yaxis_index] = list(ranges_show[1])
self.dialog.check_aspect(1)
axes.set_xlim(self.dialog.state.ranges_viewport[0])
axes.set_ylim(self.dialog.state.ranges_viewport[1])
if self.dialog.dimensions == 1:
self.dialog.state.range_level_show = range_level
axes.set_xlim(self.dialog.state.ranges_viewport[0])
axes.set_ylim(self.dialog.state.range_level_show)
self.dialog.queue_redraw()
if 0:
for axisIndex in range(self.dimensions):
linkButton = self.linkButtons[axisIndex]
link = linkButton.link
if link:
logger.debug("sending link messages")
link.sendRangesShow(self.dialog.state.ranges_viewport[axisIndex], linkButton)
link.sendPlot(linkButton)
#self.axes.set_xlim(self.xmin_show, self.xmax_show)
#self.axes.set_ylim(self.ymin_show, self.ymax_show)
#self.canvas.draw()
action = undo.ActionZoom(self.undoManager, "zoom to rect", self.set_ranges, list(range(self.dimensions)), self.dialog.ranges, self.dialog.state.ranges_viewport, self.range_level, axis_indices, ranges_viewport=ranges_show, range_level=range_level)
action.do()
self.checkUndoRedo()
if 0:
if self.autoRecalculate():
for i in range(self.dimensions):
self.dialog.ranges[i] = self.dialog.state.ranges_viewport[i]
self.range_level = None
self.compute()
self.dataset.executor.execute()
else:
self.plot()
def syncToolbar(self):
for action in [self.action_zoom]:
logger.debug("sync action: %r" % action.text())
subactions = action.menu().actions()
subaction_selected = [subaction for subaction in subactions if subaction.isChecked()]
#if len(subaction_selected) > 0:
# action.setText(subaction_selected[0].text())
# action.setIcon(subaction_selected[0].icon())
logger.debug(" subaction_selected: %r" % subaction_selected)
logger.debug(" action was selected?: %r" % action.isChecked())
action.setChecked(len(subaction_selected) > 0)
logger.debug(" action is selected?: %r" % action.isChecked())
#logger.debug("last select action: %r" % self.lastActionSelect.text())
logger.debug("last zoom action: %r" % self.lastActionZoom.text())
#self.action_select.setText(self.lastActionSelect.text())
#self.action_select.setIcon(self.lastActionSelect.icon())
self.action_zoom.setText(self.lastActionZoom.text())
self.action_zoom.setIcon(self.lastActionZoom.icon())
|
vaexioREPO_NAMEvaexPATH_START.@vaex_extracted@vaex-master@packages@vaex-ui@vaex@ui@plugin@zoom.py@.PATH_END.py
|
{
"filename": "cdf_maker.ipynb",
"repo_name": "icecube/TauRunner",
"repo_path": "TauRunner_extracted/TauRunner-master/examples/cdf_maker.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
import matplotlib as mpl
from glob import glob
import scipy.integrate as integrate
import pickle as pkl
import taurunner as tr
from taurunner.utils import units
%matplotlib inline
```
## Define your input spectrum energy and flux arrays (Energy in $GeV$ and flux is $E^{2} \Phi$ in $\rm{GeV}\, \rm{cm}^{-2}\, \rm{s^{-1}}\, \rm{sr^{-1}}$ ) ##
```python
File = np.genfromtxt(f'{tr.__path__[0]}/resources/ahlers2010.csv', delimiter = ',')
gzk_en = File[0]*units.GeV
gzk_flux = File[1]*units.GeV
gzk_mine = gzk_en[0]
gzk_maxe = gzk_en[-1]
```
## Spline the flux ##
```python
gzk_spline = UnivariateSpline(np.log10(gzk_en), np.log10(gzk_flux/gzk_en**2), k = 4, s=1e-2)
fig, ax = plt.subplots(figsize = (9,5))
test_log_ens = np.linspace(np.log10(gzk_mine), np.log10(gzk_max+0.3), 500)
plt.scatter(gzk_en/units.GeV, gzk_flux/gzk_en**2, lw = 8., alpha = 0.4, label = 'Model')
plt.plot(np.power(10, test_log_ens)/units.GeV, np.power(10., gzk_spline(test_log_ens)), label = 'Spline')
plt.yscale('log')
plt.xscale('log')
plt.xlabel(r'$E_{\nu}$ (GeV)')
plt.ylabel(r'$d\Phi / dE_{\nu}$ (GeV$^{-1}$ cm$^{-2}$ s$^{-1}$ sr$^{-1}$)')
plt.legend(loc=8, fontsize = 16)
fig.set_facecolor('w')
plt.show()
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-3-0f2d94717c93> in <module>
2
3 fig, ax = plt.subplots(figsize = (9,5))
----> 4 test_log_ens = np.linspace(np.log10(gzk_min), np.log10(gzk_max+0.3), 500)
5
6
NameError: name 'gzk_min' is not defined
## Define the probability function ##
```python
def integrand(energy):
return (10**gzk_spline(np.log10(energy)))
plt.plot((np.logspace(np.log10(gzk_min/1.2), np.log10(gzk_max*2.), 100))/units.GeV, integrand(np.logspace(np.log10(gzk_min/1.2), np.log10(gzk_max*2.), 100)))
plt.loglog()
plt.show()
```
## This should integrate to 1-ish now that since we are normalizing##
```python
integral = integrate.quad(lambda x: np.exp(x)*integrand(np.exp(x)), np.log(gzk_min), np.log(gzk_max))[0]
def probability(energy):
return integrand(energy) / integral
integ, error = integrate.quad(lambda x: np.exp(x)*probability(np.exp(x)), np.log(gzk_min), np.log(gzk_max))
print(integ)
# Plot the normalized distribution
plt.plot(np.logspace(np.log10(gzk_min), np.log10(gzk_max), 100)/units.GeV, probability(np.logspace(np.log10(gzk_min), np.log10(gzk_max), 100))*units.GeV)
plt.xlabel(r'$E_{\nu}~\left[\rm{GeV}\right]$')
plt.ylabel(r'$\frac{dN}{dE}~\left[\rm{GeV}^{-1}\right]$')
plt.loglog()
plt.show()
```
1.0000001861236076
## Make cdf ##
```python
cdf_energies = np.logspace(np.log10(gzk_min), np.log10(gzk_max*1.1), 500)
cdf = np.array([integrate.quad(lambda x: np.exp(x)*probability(np.exp(x)), np.log(gzk_min), np.log(y))[0] for y in cdf_energies])
mask = np.where(np.logical_and(cdf>0, cdf<=1))[0]
cdf = cdf[mask]
cdf_energies = cdf_energies[mask]
```
```python
plt.plot(cdf_energies/units.GeV, cdf)
plt.xscale('log')
plt.xlabel(r'$E_{\nu}~\left[\rm{GeV}\right]$')
plt.ylabel(r'Cumulative density')
plt.show()
```
```python
plt.plot(cdf, cdf_energies/units.GeV)
plt.yscale('log')
plt.ylabel(r'$E_{\nu}~\left[\rm{GeV}\right]$')
plt.xlabel(r'Cumulative density')
plt.xlim(0,1)
plt.show()
```
## Spline cdf ##
```python
cdf_spline = UnivariateSpline(cdf, cdf_energies)
```
/home/jlazar/.local/lib/python3.7/site-packages/scipy/interpolate/fitpack2.py:280: UserWarning:
A theoretically impossible result was found during the iteration
process for finding a smoothing spline with fp = s: s too small.
There is an approximation returned but the corresponding weighted sum
of squared residuals does not satisfy the condition abs(fp-s)/s < tol.
warnings.warn(message)
```python
test_cdf_x = np.linspace(0., 1., 5000)
plt.plot(test_cdf_x, cdf_spline(test_cdf_x)/units.GeV, lw = 8., alpha = 0.4, ls = '-.')
plt.plot(cdf, cdf_energies/units.GeV, ls = '-')
plt.ylabel(r'$E_{\nu}~\left[\rm{GeV}\right]$')
plt.xlabel(r'Cumulative density')
plt.yscale('log')
```
## Make sure you can sample from it and nothing looks weird. (shape of spline should match the bins) ##
```python
test_log_
```
```python
nsamples = 10000000
random_vals = np.random.uniform(low=0., high=1., size=nsamples)
injected_es = cdf_spline(random_vals)
test_es =
# Normalized arbitrarily since we only want to check that the shapes match
plt.plot(np.power(10, test_log_ens), 3e14*nsamples*np.power(10, test_log_ens)*np.power(10., gzk_spline(test_log_ens)), label = 'spline')
h = plt.hist(injected_es, bins = np.logspace(4., 12., 100)*units.GeV)
plt.semilogx()
plt.ylabel(r'Counts')
plt.xlabel(r'$E_{\nu}~\left[\rm{eV}\right]$')
plt.semilogy(nonposy='clip')
plt.legend()
plt.show()
```
/cvmfs/icecube.opensciencegrid.org/py3-v4.1.0/RHEL_7_x86_64/lib/python3.7/site-packages/ipykernel_launcher.py:14: MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
```python
out_f = f'{tr.__path__[0]}/resources/ahlers2010_test.pkl'
with open(out_f, 'wb') as pkl_f:
pkl.dump(cdf_spline, pkl_f)
```
```python
```
|
icecubeREPO_NAMETauRunnerPATH_START.@TauRunner_extracted@TauRunner-master@examples@cdf_maker.ipynb@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "MiguelEA/nudec_BSM",
"repo_path": "nudec_BSM_extracted/nudec_BSM-master/README.md",
"type": "Markdown"
}
|
# NUDEC_BSM: Neutrino Decoupling Beyond the Standard Model
This code "NUDEC_BSM", has been developed by Miguel Escudero Abenza in order to solve for early Universe thermodynamics and neutrino decoupling following the simplified approach of ArXiv:1812.05605 [JCAP 1902 (2019) 007] and ArXiv:2001.04466 [JCAP 05 (2020) 048]. If you use this code, please, cite these references.
As of 10/01/2020:
There is a Mathematica and a Python version of NUDEC_BSM. The code consists of various scripts that calculate early Universe thermodynamics in various scenarios typically within the context of neutrino decoupling.
The Python version is compatible with Python2 and Python3 and contains the following scripts:
NUDEC_BSM.py : This is a runner file that shows an example of how to run each of the models coded up in Python.
nuDec_SM.py : Solves for neutrino decoupling in the SM.
nuDec_SM_2_nu.py : Solves for neutrino decoupling in the SM evolving seperately the nu_e and nu_{mu-tau} populations.
WIMP_e.py : Solves for neutrino decoupling in the presence of a particle in thermal equilibrium with the electromagnetic sector of the plasma.
WIMP_nu.py : Solves for neutrino decoupling in the presence of a particle in thermal equilibrium with the neutrino sector of the plasma.
WIMP_generic.py : Solves for neutrino decoupling in the presence of a particle in thermal equilibrium with either the neutrino or electromagnetic sectors of the plasma, but which still interacts with the other sector by means of annihilations.
The header of each script contains the details on how to run and NUDEC_BSM.py contains an example for each case.
The Mathematica version contains the following scripts:
BasicModules.nb : Contains modules common to all models: QED finite temperature corrections, Thermodynamic formulae, SM interaction rates, constants, and parameters. When run it outputs BasicModules.m that can be loaded by any module.
Neff_SM.nb : Solves for neutrino decoupling in the Standard Model. To run one should simply run the entire script and see the examples and output.
DarkRadiation.nb : Solves for neutrino decoupling in the presence of Dark Radiation. One should simply run the entire script to find the thermodynamics. The only input parameter in this case is DNeff.
NuScalar.nb : Solves for the early Universe thermodynamics in the presence of a very light (eV<m<MeV) and weakly coupled (lambda < 10^{-9}) neutrinophilic scalar. There are two input parameters in this case: Gamma_eff and mphi (MeV). In the particular scenario considered, the results have been shown to be very accurate for Gamma_eff > 10^{-3}. Note that for Gamma_eff < 10^{-3} the accuracy could be substantially lowered.
|
MiguelEAREPO_NAMEnudec_BSMPATH_START.@nudec_BSM_extracted@nudec_BSM-master@README.md@.PATH_END.py
|
{
"filename": "lorenz.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/examples/tvtk/visual/lorenz.py",
"type": "Python"
}
|
#!/usr/bin/env python
# Author: Raashid Baig <raashid@aero.iitb.ac.in>
# License: BSD Style.
from tvtk.tools.visual import curve, box, vector, show
lorenz = curve( color = (1,1,1), radius=0.3 )
# Draw grid
for x in range(0,51,10):
curve(points = [[x,0,-25],[x,0,25]], color = (0,0.5,0), radius = 0.3 )
box(pos=(x,0,0), axis=(0,0,50), height=0.4, width=0.4, length = 50)
for z in range(-25,26,10):
curve(points = [[0,0,z], [50,0,z]] , color = (0,0.5,0), radius = 0.3 )
box(pos=(25,0,z), axis=(50,0,0), height=0.4, width=0.4, length = 50)
dt = 0.01
y = vector(35.0, -10.0, -7.0)
pts = []
for i in range(2000):
# Integrate a funny differential equation
dydt = vector( -8.0/3*y[0] + y[1]*y[2],
- 10*y[1] + 10*y[2],
- y[1]*y[0] + 28*y[1] - y[2])
y = y + dydt * dt
pts.append(y)
if len(pts) > 20:
lorenz.extend(pts)
pts[:] = []
show()
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@examples@tvtk@visual@lorenz.py@.PATH_END.py
|
{
"filename": "spec_app.py",
"repo_name": "msiebert1/UCSC_spectral_pipeline",
"repo_path": "UCSC_spectral_pipeline_extracted/UCSC_spectral_pipeline-master/spectral_reduction/spec_app.py",
"type": "Python"
}
|
import io
from base64 import b64encode
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
if __name__ == "__main__":
buffer = io.StringIO()
df = px.data.iris()
fig = px.scatter(
df, x="sepal_width", y="sepal_length",
color="species")
fig.write_html(buffer)
html_bytes = buffer.getvalue().encode()
encoded = b64encode(html_bytes).decode()
app = dash.Dash(__name__)
app.layout = html.Div([
dcc.Graph(id="graph", figure=fig),
html.A(
html.Button("Download HTML"),
id="download",
href="data:text/html;base64," + encoded,
download="plotly_graph.html"
)
])
app.run_server(debug=True)
|
msiebert1REPO_NAMEUCSC_spectral_pipelinePATH_START.@UCSC_spectral_pipeline_extracted@UCSC_spectral_pipeline-master@spectral_reduction@spec_app.py@.PATH_END.py
|
{
"filename": "population.py",
"repo_name": "deepskies/deeplenstronomy",
"repo_path": "deeplenstronomy_extracted/deeplenstronomy-master/exploded_setup_old/PopSim/population.py",
"type": "Python"
}
|
import numpy as np
import yaml
import os
config_dir = os.path.join(os.path.dirname(__file__),
'../../config_files/population/')
class Population:
def __int__(self):
pass
def load_yaml_file(self, file_name):
"""Loads configuration dictionary from yaml file"""
config_file = os.path.join(config_dir, file_name)
with open(config_file, 'r') as config_file_obj:
config_dict = yaml.safe_load(config_file_obj)
return config_dict
def draw_properties_from_models(self, model_list, config):
"""
Given a config dictionary for a list of models,
draws the needed properties
"""
kwargs = []
for model in model_list:
try:
model_config = config[model]
except KeyError:
print('Model %s configurations not specified.' % model)
raise
properties = {}
for prop in model_config:
try:
draw = np.random.uniform(model_config[prop]['min'],
model_config[prop]['max'])
except TypeError:
# if not a dict with min and max, should be a number
draw = model_config[prop]
properties[prop] = draw
kwargs.append(properties)
return kwargs
def draw_source_model(self, source_model_list=None):
"""
draws source model from population
"""
if source_model_list is None:
source_model_list = ['SERSIC_ELLIPSE']
source_config = self.load_yaml_file('source.yaml')
kwargs_source = self.draw_properties_from_models(source_model_list,
source_config)
return kwargs_source, source_model_list
def draw_lens_model(self, lens_model_list=None):
"""
draw lens model parameters
return: lens model keyword argument list, lens model list
"""
if lens_model_list is None:
lens_model_list = ['SIE', 'SHEAR']
lens_config = self.load_yaml_file('lens.yaml')
kwargs_lens = self.draw_properties_from_models(lens_model_list,
lens_config)
return kwargs_lens, lens_model_list
def draw_physical_model(self):
"""
draw physical model parameters
:return: return lens model keyword argument list, lens model list
"""
from astropy.cosmology import FlatLambdaCDM
from lenstronomy.SimulationAPI.model_api import ModelAPI
# redshift
z_lens = np.random.uniform(0.1, 10.)
z_source = np.random.uniform(0.1, 10.)
z_source_convention = 3.
# cosmology
omega_m = np.random.uniform(1e-9, 1)
H0 = 70.
omega_bar = 0.0
cosmo = FlatLambdaCDM(H0=H0, Om0=omega_m, Ob0=omega_bar)
# Lens physical parameters: Alternative A
sigma_v = np.random.uniform(10., 1000.)
lens_e1 = (np.random.uniform() - 0.5) * 0.8
lens_e2 = (np.random.uniform() - 0.5) * 0.8
# Lens physical parameters: Alternative B
mass_scale = 1.e13
M200 = np.random.uniform(1., 100) * mass_scale
concentration = np.random.uniform(1, 7)
# Models
lens_model_list = ['SIE', 'SHEAR']
# kwargs: this is for single-plane lensing
kwargs_model_lensing = {
'lens_model_list': lens_model_list, # list of lens models to be used
'z_lens': z_lens, # list of redshift of the deflections
'z_source': z_source, # redshift of the default source (if not further specified by 'source_redshift_list') and also serves as the redshift of lensed point sources
'z_source_convention': z_source_convention, # source redshfit to which the reduced deflections are computed, is the maximal redshift of the ray-tracing
'cosmo': cosmo # astropy.cosmology instance
}
# kwargs: mass
kwargs_mass = [{'sigma_v': sigma_v, 'center_x': 0, 'center_y': 0, 'e1': lens_e1, 'e2': lens_e2},
{'M200': M200, 'concentration': concentration, 'center_x': 0, 'center_y': 0}]
# Model API
sim = ModelAPI(**kwargs_model_lensing)
# convert from physical values to reduced lensing values
kwargs_lens = sim.physical2lensing_conversion(kwargs_mass=kwargs_mass)
return kwargs_lens, lens_model_list
def draw_lens_light(self):
"""
:return:
"""
lens_light_model_list = ['SERSIC_ELLIPSE']
kwargs_lens_light = [{'magnitude': 22, 'R_sersic': 0.3, 'n_sersic': 1, 'e1': -0.3, 'e2': -0.2, 'center_x': 0, 'center_y': 0}]
return kwargs_lens_light, lens_light_model_list
def draw_point_source(self, center_x, center_y):
"""
:param center_x: center of point source in source plane
:param center_y: center of point source in source plane
:return:
"""
point_source_model_list = ['SOURCE_POSITION']
kwargs_ps = [{'magnitude': 21, 'ra_source': center_x, 'dec_source': center_y}]
return kwargs_ps, point_source_model_list
def _simple_draw(self, with_lens_light=False, with_quasar=False, **kwargs):
"""
:param with_lens_light:
:param with_quasar:
:param kwargs:
:return:
"""
# lens
kwargs_lens, lens_model_list = self.draw_lens_model()
kwargs_params = {'kwargs_lens': kwargs_lens}
kwargs_model = {'lens_model_list': lens_model_list}
# source
kwargs_source, source_model_list = self.draw_source_model()
kwargs_params['kwargs_source_mag'] = kwargs_source
kwargs_model['source_light_model_list'] = source_model_list
# for toggling with injection simulations
if with_lens_light:
kwargs_lens_light, lens_light_model_list = self.draw_lens_light()
kwargs_params['kwargs_lens_light_mag'] = kwargs_lens_light
kwargs_model['lens_light_model_list'] = lens_light_model_list
# for toggling a quasar
if with_quasar:
kwargs_ps, point_source_model_list = self.draw_point_source(center_x=kwargs_source[0]['center_x'],
center_y=kwargs_source[0]['center_y'])
kwargs_params['kwargs_ps_mag'] = kwargs_ps
kwargs_model['point_source_model_list'] = point_source_model_list
return kwargs_params, kwargs_model
def _complex_draw(self, with_lens_light=False, with_quasar=False, **kwargs):
"""
:param with_lens_light:
:param with_quasar:
:param kwargs:
:return:
"""
kwargs_lens, lens_model_list = self.draw_physical_model()
kwargs_source, source_model_list = self.draw_source_model()
kwargs_params = {'kwargs_lens': kwargs_lens,
'kwargs_source_mag': kwargs_source}
kwargs_model = {'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list}
# for toggling with injection simulations
if with_lens_light:
kwargs_lens_light, lens_light_model_list = self.draw_lens_light()
kwargs_params['kwargs_lens_light_mag'] = kwargs_lens_light
kwargs_model['lens_light_model_list'] = lens_light_model_list
# for toggling a quasar
if with_quasar:
kwargs_ps, point_source_model_list = self.draw_point_source(center_x=kwargs_source[0]['center_x'],
center_y=kwargs_source[0]['center_y'])
kwargs_params['kwargs_ps_mag'] = kwargs_ps
kwargs_model['point_source_model_list'] = point_source_model_list
return kwargs_params, kwargs_model
def draw_model(self, with_lens_light=False, with_quasar=False, mode='simple', **kwargs):
"""
returns all keyword arguments of the model
:param kwargs:
:return: kwargs_params, kwargs_model
"""
if mode == 'simple':
return self._simple_draw(with_lens_light, with_quasar, **kwargs)
if mode == 'complex':
return self._complex_draw(with_lens_light, with_quasar, **kwargs)
else:
raise ValueError('mode %s is not supported!' % mode)
|
deepskiesREPO_NAMEdeeplenstronomyPATH_START.@deeplenstronomy_extracted@deeplenstronomy-master@exploded_setup_old@PopSim@population.py@.PATH_END.py
|
{
"filename": "dbutils.py",
"repo_name": "sdss/marvin",
"repo_path": "marvin_extracted/marvin-main/python/marvin/utils/db/dbutils.py",
"type": "Python"
}
|
import marvin
import traceback
import sys
import inspect
# This line makes sure that "from marvin.utils.db.dbutils import *"
# will only import the functions in the list.
__all__ = ['get_traceback', 'testDbConnection', 'generateClassDict']
def get_traceback(asstring=None):
''' Returns the traceback from an exception, a list
Parameters:
asstring = boolean to return traceback as a joined string
'''
ex_type, ex_info, tb = sys.exc_info()
newtb = traceback.format_tb(tb)
return ' '.join(newtb) if asstring else newtb
def testDbConnection(session=None):
''' Test the DB connection to '''
res = {'good': None, 'error': None}
if not session:
session = marvin.marvindb.session
try:
tmp = session.query(marvin.marvindb.datadb.PipelineVersion).first()
res['good'] = True
except Exception as e:
error1 = 'Error connecting to manga database: {0}'.format(str(e))
tb = get_traceback(asstring=True)
error2 = 'Full traceback: {0}'.format(tb)
error = ' '.join([error1, error2])
res['error'] = error
return res
def generateClassDict(modelclasses, lower=None):
''' Generates a dictionary of the Model Classes, based on class name as key, to the object class.
Selects only those classes in the module with attribute __tablename__
lower = True makes class name key all lowercase
'''
classdict = {}
for model in inspect.getmembers(modelclasses, inspect.isclass):
keyname = model[0].lower() if lower else model[0]
if hasattr(model[1], '__tablename__'):
classdict[keyname] = model[1]
return classdict
|
sdssREPO_NAMEmarvinPATH_START.@marvin_extracted@marvin-main@python@marvin@utils@db@dbutils.py@.PATH_END.py
|
{
"filename": "_family.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/funnel/insidetextfont/_family.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FamilyValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="family", parent_name="funnel.insidetextfont", **kwargs
):
super(FamilyValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "calc"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@funnel@insidetextfont@_family.py@.PATH_END.py
|
{
"filename": "testSharedElements.py",
"repo_name": "LLNL/spheral",
"repo_path": "spheral_extracted/spheral-main/tests/unit/Mesh/testSharedElements.py",
"type": "Python"
}
|
import mpi
from Spheral2d import *
#----------------------------------------------------------------------
# Test that the shared nodes are consisten between domains.
#----------------------------------------------------------------------
def testSharedNodes(mesh):
assert len(mesh.neighborDomains) == len(mesh.sharedNodes)
# First check that everyone agrees about who is talking to who.
myNeighborDomains = list(mesh.neighborDomains)
for sendProc in range(mpi.procs):
otherProcs = mpi.bcast(myNeighborDomains, root=sendProc)
if mpi.rank != sendProc:
assert (mpi.rank in otherProcs) == (sendProc in mesh.neighborDomains)
# Build our set of global shared node IDs.
globalIDs = mesh.globalMeshNodeIDs()
globalSharedNodes = [[globalIDs[i] for i in localNodes] for localNodes in mesh.sharedNodes]
assert len(globalSharedNodes) == len(mesh.neighborDomains)
# Check that the shared nodes are consistent.
sendRequests = []
for (otherProc, ids) in zip(mesh.neighborDomains, globalSharedNodes):
sendRequests.append(mpi.isend(ids, dest=otherProc))
for (otherProc, ids) in zip(mesh.neighborDomains, globalSharedNodes):
otherIDs = mpi.recv(source=otherProc)[0]
assert ids == otherIDs
# Check that all shared nodes have been found.
localSharedNodes = [[i for i in localNodes] for localNodes in mesh.sharedNodes]
positions = vector_of_Vector()
for i in range(mesh.numNodes):
positions.append(mesh.node(i).position())
xmin, xmax = Vector(), Vector()
boundingBox(positions, xmin, xmax)
xmin = Vector(mpi.allreduce(xmin.x, mpi.MIN), mpi.allreduce(xmin.y, mpi.MIN))
xmax = Vector(mpi.allreduce(xmax.x, mpi.MAX), mpi.allreduce(xmax.y, mpi.MAX))
boxInv = Vector(1.0/(xmax.x - xmin.x),
1.0/(xmax.y - xmin.y))
nodeHashes = [hashPosition(mesh.node(i).position(), xmin, xmax, boxInv) for i in range(mesh.numNodes)]
nodeHashes2ID = {}
for i in range(len(nodeHashes)):
nodeHashes2ID[nodeHashes[i]] = i
for sendProc in range(mpi.procs):
otherNodeHashes = mpi.bcast(nodeHashes, root=sendProc)
if sendProc != mpi.rank:
for hashi in otherNodeHashes:
if hashi in nodeHashes:
assert sendProc in myNeighborDomains
idomain = myNeighborDomains.index(sendProc)
i = nodeHashes2ID[hashi]
assert i in localSharedNodes[idomain]
# Same for faces.
localSharedFaces = [[i for i in localFaces] for localFaces in mesh.sharedFaces]
positions = vector_of_Vector()
for i in range(mesh.numFaces):
positions.append(mesh.face(i).position())
faceHashes = [hashPosition(mesh.face(i).position(), xmin, xmax, boxInv) for i in range(mesh.numFaces)]
faceHashes2ID = {}
for i in range(len(faceHashes)):
faceHashes2ID[faceHashes[i]] = i
for sendProc in range(mpi.procs):
otherFaceHashes = mpi.bcast(faceHashes, root=sendProc)
if sendProc != mpi.rank:
for hashi in otherFaceHashes:
if hashi in faceHashes:
assert sendProc in myNeighborDomains
idomain = myNeighborDomains.index(sendProc)
i = faceHashes2ID[hashi]
assert i in localSharedFaces[idomain]
return True
|
LLNLREPO_NAMEspheralPATH_START.@spheral_extracted@spheral-main@tests@unit@Mesh@testSharedElements.py@.PATH_END.py
|
{
"filename": "[7]cluster mockup-checkpoint.ipynb",
"repo_name": "jan-rybizki/Galaxia_wrap",
"repo_path": "Galaxia_wrap_extracted/Galaxia_wrap-master/notebook/.ipynb_checkpoints/[7]cluster mockup-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
```python
from astropy.coordinates import SkyCoord, ICRS, CartesianRepresentation, CartesianDifferential, Galactic, Galactocentric
import astropy.units as u
from astropy.io import fits
%pylab inline
import ebf
import shutil
import subprocess
import os, sys
path = os.path.abspath('../library/')
if path not in sys.path:
sys.path.append(path)
from convert_to_recarray import create_gdr2mock_mag_limited_survey_from_nbody
```
Populating the interactive namespace from numpy and matplotlib
```python
"""
Plummer model generator
This module contains a function used to create Plummer (1911) models, which
follow a spherically symmetric density profile of the form:
rho = c * (1 + r**2)**(-5/2)
"""
import numpy
import numpy.random
from math import pi, sqrt
from amuse.units import nbody_system
from amuse import datamodel
__all__ = ["new_plummer_sphere", "new_plummer_model"]
class MakePlummerModel(object):
def __init__(self, number_of_particles, convert_nbody = None, radius_cutoff = 22.8042468, mass_cutoff = 0.999,
do_scale = False, random_state = None, random = None):
self.number_of_particles = number_of_particles
self.convert_nbody = convert_nbody
self.mass_cutoff = min(mass_cutoff, self.calculate_mass_cuttof_from_radius_cutoff(radius_cutoff))
self.do_scale = do_scale
if not random_state == None:
print("DO NOT USE RANDOM STATE")
self.random_state = None
if random is None:
self.random = numpy.random
else:
self.random = random
def calculate_mass_cuttof_from_radius_cutoff(self, radius_cutoff):
if radius_cutoff > 99999:
return 1.0
scale_factor = 16.0 / (3.0 * pi)
rfrac = radius_cutoff * scale_factor
denominator = pow(1.0 + rfrac ** 2, 1.5)
numerator = rfrac ** 3
return numerator/denominator
def calculate_radius(self, index):
mass_min = (index * self.mass_cutoff) / self.number_of_particles
mass_max = ((index+1) * self.mass_cutoff) / self.number_of_particles
random_mass_fraction = self.random.uniform(mass_min, mass_max)
radius = 1.0 / sqrt( pow (random_mass_fraction, -2.0/3.0) - 1.0)
return radius
def calculate_radius_uniform_distribution(self):
return 1.0 / numpy.sqrt( numpy.power(self.random.uniform(0,self.mass_cutoff,(self.number_of_particles,1)), -2.0/3.0) - 1.0)
def new_positions_spherical_coordinates(self):
pi2 = pi * 2
radius = self.calculate_radius_uniform_distribution()
theta = numpy.arccos(self.random.uniform(-1.0,1.0, (self.number_of_particles,1)))
phi = self.random.uniform(0.0,pi2, (self.number_of_particles,1))
return (radius,theta,phi)
def new_velocities_spherical_coordinates(self, radius):
pi2 = pi * 2
x,y = self.new_xy_for_velocity()
velocity = x * sqrt(2.0) * numpy.power( 1.0 + radius*radius, -0.25)
theta = numpy.arccos(self.random.uniform(-1.0,1.0, (self.number_of_particles,1)))
phi = self.random.uniform(0.0,pi2, (self.number_of_particles,1))
return (velocity,theta,phi)
def coordinates_from_spherical(self, radius, theta, phi):
x = radius * numpy.sin( theta ) * numpy.cos( phi )
y = radius * numpy.sin( theta ) * numpy.sin( phi )
z = radius * numpy.cos( theta )
return (x,y,z)
def new_xy_for_velocity(self):
number_of_selected_items = 0
selected_values_for_x = numpy.zeros(0)
selected_values_for_y = numpy.zeros(0)
while (number_of_selected_items < self.number_of_particles):
x = self.random.uniform(0,1.0, (self.number_of_particles-number_of_selected_items))
y = self.random.uniform(0,0.1, (self.number_of_particles-number_of_selected_items))
g = (x**2) * numpy.power(1.0 - x**2, 3.5)
compare = y <= g
selected_values_for_x = numpy.concatenate((selected_values_for_x, x.compress(compare)))
selected_values_for_y= numpy.concatenate((selected_values_for_x, y.compress(compare)))
number_of_selected_items = len(selected_values_for_x)
return numpy.atleast_2d(selected_values_for_x).transpose(), numpy.atleast_2d(selected_values_for_y).transpose()
def new_model(self):
m = numpy.zeros((self.number_of_particles,1)) + (1.0 / self.number_of_particles)
radius, theta, phi = self.new_positions_spherical_coordinates()
position = numpy.hstack(self.coordinates_from_spherical(radius, theta, phi))
radius, theta, phi = self.new_velocities_spherical_coordinates(radius)
velocity = numpy.hstack(self.coordinates_from_spherical(radius, theta, phi))
position = position / 1.695
velocity = velocity / sqrt(1 / 1.695)
return (m, position, velocity)
@property
def result(self):
masses = numpy.ones(self.number_of_particles) / self.number_of_particles
radius, theta, phi = self.new_positions_spherical_coordinates()
x,y,z = self.coordinates_from_spherical(radius, theta, phi)
radius, theta, phi = self.new_velocities_spherical_coordinates(radius)
vx,vy,vz = self.coordinates_from_spherical(radius, theta, phi)
result = datamodel.Particles(self.number_of_particles)
result.mass = nbody_system.mass.new_quantity(masses)
result.x = nbody_system.length.new_quantity(x.reshape(self.number_of_particles)/1.695)
result.y = nbody_system.length.new_quantity(y.reshape(self.number_of_particles)/1.695)
result.z = nbody_system.length.new_quantity(z.reshape(self.number_of_particles)/1.695)
result.vx = nbody_system.speed.new_quantity(vx.reshape(self.number_of_particles) / sqrt(1/1.695))
result.vy = nbody_system.speed.new_quantity(vy.reshape(self.number_of_particles) / sqrt(1/1.695))
result.vz = nbody_system.speed.new_quantity(vz.reshape(self.number_of_particles) / sqrt(1/1.695))
result.radius = 0 | nbody_system.length
result.move_to_center()
if self.do_scale:
result.scale_to_standard()
if not self.convert_nbody is None:
result = datamodel.ParticlesWithUnitsConverted(result, self.convert_nbody.as_converter_from_si_to_generic())
result = result.copy()
return result
def new_plummer_model(number_of_particles, *list_arguments, **keyword_arguments):
"""
Create a plummer sphere with the given number of particles. Returns
a set of stars with equal mass and positions and velocities distributed
to fit a plummer star distribution model. The model is centered around the
origin. Positions and velocities are optionally scaled such that the kinetic and
potential energies are 0.25 and -0.5 in nbody-units, respectively.
:argument number_of_particles: Number of particles to include in the plummer sphere
:argument convert_nbody: When given will convert the resulting set to SI units
:argument radius_cutoff: Cutoff value for the radius (defaults to 22.8042468)
:argument mass_cutoff: Mass percentage inside radius of 1
:argument do_scale: scale the result to exact nbody units (M=1, K=0.25, U=-0.5)
"""
uc = MakePlummerModel(number_of_particles, *list_arguments, **keyword_arguments)
return uc.result
new_plummer_sphere = new_plummer_model
```
```python
i=0
cat = fits.getdata('../input/fake_cluster/cluster_for_processing.fits')
for item in cat.dtype.names:
print(item,cat[item][i])
#cat = cat[:1]
```
clusterName Lynga_15
ra 175.523
dec -62.457
r50 3.6273161956
pmra -6.477
pmdec 0.793
age_gyr 0.0251188643151
FeH_synth 0.0499636163811
rvs 0.0166786820078
mass 301.274217933
vrot 0.100198746965
distance_pc 1717.6
x0 -0.438507686583
y0 0.109971617499
z0 0.891973795665
```python
nbody_folder = '/home/rybizki/Programme/GalaxiaData/'
folder = 'cluster/'
filename = 'cluster_list'
folder_cat = '../output/Clusters'
# Need to specify where the GalaxiaData folder is and how to name the new simulation
names = cat.clusterName
# 2 input files for Galaxia need to be created
folder_create = nbody_folder + 'nbody1/' + folder
if os.path.exists(folder_create):
shutil.rmtree(folder_create)
os.mkdir(folder_create)
print(folder_create, "existed and was recreated")
else:
os.mkdir(folder_create)
# Here the file which tells Galaxia where to find the input file is created
filedata = 'nbody1/%s\n %d 1\n' %(folder,len(names))
for item in names:
filedata += '%s%s.ebf\n' %(filename,item.decode())
#filedata = 'nbody1/%s\n %d 1\n%s.ebf\n' %(folder,1, filename + names[1])
file = open(nbody_folder + "nbody1/filenames/" + filename + ".txt", "w")
file.write(filedata)
file.close()
```
/home/rybizki/Programme/GalaxiaData/nbody1/cluster/ existed and was recreated
```python
import numpy as np
from amuse.units import units
```
```python
for i in range(len(cat)):
print(i,names[i].decode(), len(cat))
ra = cat.ra[i]
dec = cat.dec[i]
distance = cat.distance_pc[i]
pmra = cat.pmra[i]
pmdec = cat.pmdec[i]
rvs = cat.rvs[i]
age = cat.age_gyr[i]
feh = cat.FeH_synth[i]
mass = cat.mass[i]
name = cat.clusterName[i].decode()
#Galactocentric coordinate system
c = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, distance=(distance/1000.)*u.kpc, frame='icrs',
pm_ra_cosdec = pmra*u.mas/u.yr, pm_dec = pmdec*u.mas/u.yr, radial_velocity = rvs*u.km/u.s,
galcen_distance = 8.0*u.kpc, z_sun = 15.0*u.pc,
galcen_v_sun=CartesianDifferential(d_x=11.1*u.km/u.s, d_y=239.08*u.km/u.s, d_z=7.25*u.km/u.s))
pos_x = c.galactocentric.x.value
pos_y = c.galactocentric.y.value
pos_z = c.galactocentric.z.value
vel_x = c.galactocentric.v_x.value
vel_y = c.galactocentric.v_y.value
vel_z = c.galactocentric.v_z.value
# setting the physical lengthscale
l_scale = cat.r50[i]
# random spin axis vector
x0 = cat.x0[i]
y0 = cat.y0[i]
z0 = cat.z0[i]
#Generating nbody particles from cluster information
Mcluster = mass | units.MSun
Rcluster= l_scale | units.parsec
converter= nbody_system.nbody_to_si(Mcluster,Rcluster)
nparticles = 1000
stars = new_plummer_sphere(nparticles,converter)
nbx =stars.x.value_in(units.pc)
nby =stars.y.value_in(units.pc)
nbz =stars.z.value_in(units.pc)
nbmass = stars.mass.value_in(units.MSun)
nbvx = stars.vx.value_in(units.kms)
nbvy = stars.vy.value_in(units.kms)
nbvz = stars.vz.value_in(units.kms)
#distance_to_rot_axis = |x0_vec x nbx_vec| (for line through origin and spin axis being normed)
resx = y0*nbz - z0*nby
resy = z0*nbx - x0*nbz
resz = x0*nby - y0*nbx
# this then scales the rotational velocity
dist_spin_axis = np.sqrt(resx**2+resy**2+resz**2)
vrot = np.divide(l_scale,dist_spin_axis)*cat.vrot[i]
# direction of the rotation (perpendicular to spin axis and the point connected to origin)
normx = np.divide(resx,dist_spin_axis)
normy = np.divide(resy,dist_spin_axis)
normz = np.divide(resz,dist_spin_axis)
# speed xyz
speed_x = normx*vrot
speed_y = normy*vrot
speed_z = normz*vrot
# rescale nbxyz vectors to kpc and add galactocentric xyz
nbx = np.divide(nbx,1000) + pos_x
nby = np.divide(nby,1000) + pos_y
nbz = np.divide(nbz,1000) + pos_z
nbspeed_x = speed_x + vel_x + nbvx
nbspeed_y = speed_y + vel_y + nbvy
nbspeed_z = speed_z + vel_z + nbvz
# galaxia format
pos = np.zeros((nparticles,3))
vel = np.zeros((nparticles,3))
pos[:,0] = nbx
pos[:,1] = nby
pos[:,2] = nbz
vel[:,0] = nbspeed_x
vel[:,1] = nbspeed_y
vel[:,2] = nbspeed_z
# assign age
nbage = np.linspace(age-0.0009,age+0.0009,num = nparticles)
nbfeh = np.sort(np.random.normal(feh,0.01,nparticles))
nbage = nbage[::-1] # check if that is necessary and results in the right age
nbalpha = np.zeros(nparticles)
# writing to ebf file for galaxia
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/mass', nbmass,'w')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/feh', nbfeh,'a')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/id', 1,'a')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/alpha', nbalpha,'a')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/age', nbage,'a')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/pos3', pos,'a')
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '.ebf', '/vel3', vel,'a')
# Preparing file for Enbid
ps = np.concatenate((pos,vel),axis = 1)
enbid_filename = '%s.dat' %(name)
np.savetxt(enbid_filename,ps,fmt='%.6f')
# Making the parameterfile for Enbid
filedata = 'InitCondFile %s\nICFormat 0 \nSnapshotFileBase _ph3\nSpatialScale 1 \nPartBoundary 7 \nNodeSplittingCriterion 1 \nCubicCells 1 \nMedianSplittingOn 0 \nTypeOfSmoothing 3\nDesNumNgb 64 \nVolCorr 1 \nTypeOfKernel 3 \nKernelBiasCorrection 1 \nAnisotropicKernel 0 \nAnisotropy 0 \nDesNumNgbA 128 \nTypeListOn 0\nPeriodicBoundaryOn 0 \n\n' %(enbid_filename)
myparameterfile = "myparameterfile3"
file = open(myparameterfile, "w")
file.write(filedata)
file.close()
#Running Enbid
args = ['./Enbid', myparameterfile]
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#print("Enbid calculates smoothing length")
(output, err) = p.communicate()
# Writing to nbody smoothing length file
t = np.genfromtxt(enbid_filename + "_ph3.est",skip_header=1)
d6 = np.zeros((len(pos),2))
d6[:,0] = t[:,1]
d6[:,1] = t[:,2]
#print(d6.shape,t.shape)
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '_d6n64_den.ebf', '/h_cubic', d6, 'w')
# remove temporary files
os.remove(enbid_filename)
os.remove(enbid_filename + "_ph3.est")
# Same for 3d
# Preparing file for Enbid
enbid_filename = '%s3d.dat' %(name)
np.savetxt(enbid_filename,pos,fmt='%.6f')
# Making the parameterfile for Enbid
filedata = 'InitCondFile %s\nICFormat 0 \nSnapshotFileBase _ph3\nSpatialScale 1 \nPartBoundary 7 \nNodeSplittingCriterion 1 \nCubicCells 1 \nMedianSplittingOn 0 \nTypeOfSmoothing 3\nDesNumNgb 64 \nVolCorr 1 \nTypeOfKernel 3 \nKernelBiasCorrection 1 \nAnisotropicKernel 0 \nAnisotropy 0 \nDesNumNgbA 128 \nTypeListOn 0\nPeriodicBoundaryOn 0 \n\n' %(enbid_filename)
myparameterfile = "myparameterfile3"
file = open(myparameterfile, "w")
file.write(filedata)
file.close()
#Running Enbid
args = ['./Enbid3d', myparameterfile]
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#print("Enbid calculates smoothing length")
(output, err) = p.communicate()
# Writing to nbody smoothing length file
t = np.genfromtxt(enbid_filename + "_ph3.est",skip_header=1)
d3 = np.zeros((len(pos)))
d3 = t[:,1]
#print(d3.shape,t.shape)
ebf.write(nbody_folder + 'nbody1/' + folder + filename + name + '_d3n64_den.ebf', '/h_cubic', d3, 'w')
# remove temporary files
os.remove(enbid_filename)
os.remove(enbid_filename + "_ph3.est")
```
0 Lynga_15 1118
1 FSR_1051 1118
2 FSR_1397 1118
3 Ruprecht_43 1118
4 SAI_72 1118
5 ESO_313_03 1118
6 Czernik_26 1118
7 Berkeley_14A 1118
8 Stock_17 1118
9 DBSB_21 1118
10 FSR_1025 1118
11 FSR_1580 1118
12 Ruprecht_24 1118
13 Teutsch_42 1118
14 Berkeley_29 1118
15 FSR_0401 1118
16 FSR_0975 1118
17 FSR_1419 1118
18 Koposov_43 1118
19 FSR_1172 1118
20 Ivanov_8 1118
21 Alessi_15 1118
22 Alessi_59 1118
23 Basel_10 1118
24 ESO_559_13 1118
25 FSR_1460 1118
26 Ruprecht_32 1118
27 FSR_0284 1118
28 FSR_0524 1118
29 FSR_1335 1118
30 Hogg_10 1118
31 NGC_1624 1118
32 SAI_47 1118
33 Teutsch_13 1118
34 Teutsch_52 1118
35 Turner_3 1118
36 Kronberger_54 1118
37 Stock_13 1118
38 Teutsch_50 1118
39 Czernik_43 1118
40 FSR_1171 1118
41 Graham_1 1118
42 Mamajek_1 1118
43 Schuster_1 1118
44 Teutsch_27 1118
45 FSR_1032 1118
46 Ivanov_4 1118
47 NGC_1444 1118
48 Platais_10 1118
49 Teutsch_31 1118
50 ASCC_66 1118
51 Berkeley_102 1118
52 ESO_393_15 1118
53 FSR_0977 1118
54 FSR_1352 1118
55 Ruprecht_77 1118
56 DB2001_22 1118
57 Patchick_90 1118
58 Berkeley_25 1118
59 Hogg_18 1118
60 Skiff_J0458+43.0 1118
61 Teutsch_11 1118
62 DBSB_6 1118
63 FSR_1170 1118
64 Juchert_19 1118
65 Lynga_14 1118
66 Patchick_75 1118
67 Skiff_J0619+18.5 1118
68 Stock_18 1118
69 Alessi_17 1118
70 Alessi_18 1118
71 Berkeley_82 1118
72 Bochum_4 1118
73 DBSB_104 1118
74 FSR_0158 1118
75 FSR_0465 1118
76 Markarian_38 1118
77 Czernik_6 1118
78 DBSB_43 1118
79 ESO_211_09 1118
80 FSR_1297 1118
81 Teutsch_66 1118
82 Teutsch_8 1118
83 Toepler_1 1118
84 Turner_5 1118
85 BH_111 1118
86 Kronberger_57 1118
87 Teutsch_125 1118
88 ESO_226_06 1118
89 FSR_1363 1118
90 Ruprecht_10 1118
91 Basel_17 1118
92 Berkeley_5 1118
93 Berkeley_86 1118
94 DBSB_60 1118
95 DC_8 1118
96 Dolidze_11 1118
97 FSR_0833 1118
98 FSR_1117 1118
99 Teutsch_30 1118
100 Teutsch_54 1118
101 BDSB91 1118
102 Bochum_3 1118
103 Collinder_469 1118
104 Juchert_18 1118
105 Kronberger_85 1118
106 NGC_1724 1118
107 Pfleiderer_3 1118
108 SAI_149 1118
109 Arp_Madore_2 1118
110 BDSB30 1118
111 BDSB93 1118
112 Basel_8 1118
113 FSR_0238 1118
114 FSR_0683 1118
115 FSR_0905 1118
116 Havlen_Moffat_1 1118
117 LDN_988e 1118
118 Lynga_1 1118
119 NGC_2580 1118
120 NGC_7058 1118
121 Pismis_17 1118
122 Ruprecht_25 1118
123 Skiff_J2330+60.2 1118
124 FSR_0536 1118
125 Patchick_94 1118
126 Ruprecht_61 1118
127 SAI_17 1118
128 Teutsch_7 1118
129 vdBergh_85 1118
130 Antalova_2 1118
131 FSR_0826 1118
132 FSR_0852 1118
133 NGC_7024 1118
134 BH_151 1118
135 Dolidze_3 1118
136 ESO_166_04 1118
137 FSR_0430 1118
138 FSR_0968 1118
139 FSR_1125 1118
140 FSR_1484 1118
141 NGC_225 1118
142 NGC_6800 1118
143 NGC_7129 1118
144 ASCC_97 1118
145 Berkeley_20 1118
146 Berkeley_34 1118
147 ESO_368_14 1118
148 FSR_0667 1118
149 FSR_1183 1118
150 Feibelman_1 1118
151 Ruprecht_29 1118
152 Sher_1 1118
153 Teutsch_23 1118
154 Berkeley_83 1118
155 Czernik_12 1118
156 Dolidze_32 1118
157 Kronberger_1 1118
158 Lynga_3 1118
159 SAI_25 1118
160 ASCC_115 1118
161 Alessi_53 1118
162 Berkeley_66 1118
163 Berkeley_92 1118
164 Czernik_1 1118
165 ESO_312_04 1118
166 FSR_1399 1118
167 FSR_1452 1118
168 FSR_1509 1118
169 IC_2157 1118
170 Juchert_9 1118
171 Berkeley_103 1118
172 Czernik_8 1118
173 Muzzio_1 1118
174 Pismis_27 1118
175 Trumpler_34 1118
176 Waterloo_7 1118
177 Berkeley_91 1118
178 FSR_0542 1118
179 FSR_0883 1118
180 Kronberger_80 1118
181 Ruprecht_151 1118
182 Czernik_10 1118
183 Czernik_20 1118
184 Dolidze_53 1118
185 FSR_0296 1118
186 NGC_2367 1118
187 Ruprecht_76 1118
188 Saurer_2 1118
189 Trumpler_33 1118
190 BH_66 1118
191 BH_92 1118
192 Dolidze_8 1118
193 BH_245 1118
194 FSR_0921 1118
195 FSR_1063 1118
196 FSR_1284 1118
197 Kharchenko_1 1118
198 Kronberger_84 1118
199 Loden_46 1118
200 Ruprecht_108 1118
201 SAI_16 1118
202 Teutsch_103 1118
203 Berkeley_61 1118
204 Berkeley_65 1118
205 DBSB_101 1118
206 DBSB_3 1118
207 FSR_0553 1118
208 FSR_1260 1118
209 Haffner_3 1118
210 Hogg_19 1118
211 Kronberger_69 1118
212 Markarian_50 1118
213 NGC_5606 1118
214 NGC_6178 1118
215 Patchick_3 1118
216 Ruprecht_148 1118
217 SAI_14 1118
218 Teutsch_44 1118
219 ASCC_67 1118
220 FSR_1150 1118
221 FSR_1212 1118
222 NGC_2588 1118
223 Stock_14 1118
224 Turner_9 1118
225 FSR_0384 1118
226 FSR_0448 1118
227 Kronberger_81 1118
228 NGC_7160 1118
229 Pismis_11 1118
230 Ruprecht_97 1118
231 Alessi_1 1118
232 Alessi_13 1118
233 Czernik_3 1118
234 Czernik_44 1118
235 Ruprecht_50 1118
236 BH_54 1118
237 Berkeley_75 1118
238 Czernik_42 1118
239 FSR_0948 1118
240 King_16 1118
241 NGC_6613 1118
242 Ruprecht_71 1118
243 BH_78 1118
244 FSR_0172 1118
245 FSR_0357 1118
246 FSR_1530 1118
247 Haffner_19 1118
248 King_17 1118
249 Koposov_53 1118
250 NGC_1333 1118
251 Roslund_4 1118
252 Ruprecht_35 1118
253 SAI_108 1118
254 Stock_20 1118
255 Teutsch_61 1118
256 ASCC_87 1118
257 Auner_1 1118
258 BH_67 1118
259 Berkeley_73 1118
260 Collinder_419 1118
261 Czernik_18 1118
262 FSR_0811 1118
263 FSR_0935 1118
264 NGC_2269 1118
265 NGC_5764 1118
266 NGC_7226 1118
267 Ruprecht_102 1118
268 Ruprecht_19 1118
269 SAI_4 1118
270 Berkeley_101 1118
271 Berkeley_104 1118
272 Berkeley_21 1118
273 Berkeley_76 1118
274 Berkeley_94 1118
275 DBSB_100 1118
276 Dolidze_16 1118
277 FSR_0537 1118
278 FSR_0551 1118
279 FSR_0923 1118
280 FSR_1435 1118
281 Stock_16 1118
282 Teutsch_106 1118
283 vdBergh_83 1118
284 BH_19 1118
285 Collinder_271 1118
286 Koposov_63 1118
287 Negueruela_1 1118
288 Riddle_4 1118
289 ASCC_29 1118
290 Czernik_16 1118
291 Dias_2 1118
292 Haffner_20 1118
293 NGC_5281 1118
294 Pismis_8 1118
295 Ruprecht_105 1118
296 Ruprecht_130 1118
297 ASCC_123 1118
298 Alessi_8 1118
299 BH_150 1118
300 Basel_4 1118
301 ESO_092_05 1118
302 Hogg_17 1118
303 Pismis_24 1118
304 Ruprecht_75 1118
305 Trumpler_18 1118
306 Berkeley_63 1118
307 FSR_0195 1118
308 Mayer_1 1118
309 NGC_1579 1118
310 Ruprecht_135 1118
311 Berkeley_1 1118
312 Czernik_27 1118
313 FSR_0985 1118
314 FSR_1441 1118
315 FSR_1595 1118
316 Harvard_13 1118
317 NGC_1220 1118
318 NGC_189 1118
319 NGC_6469 1118
320 NGC_7281 1118
321 Skiff_J0614+12.9 1118
322 Czernik_14 1118
323 FSR_0398 1118
324 Hogg_21 1118
325 NGC_1496 1118
326 Ruprecht_144 1118
327 Waterloo_1 1118
328 ASCC_107 1118
329 ASCC_30 1118
330 BH_73 1118
331 ESO_092_18 1118
332 FSR_1207 1118
333 FSR_1380 1118
334 Barkhatova_1 1118
335 FSR_0932 1118
336 FSR_1360 1118
337 NGC_2225 1118
338 NGC_2302 1118
339 NGC_6249 1118
340 Ruprecht_33 1118
341 Ruprecht_67 1118
342 Teutsch_14a 1118
343 Berkeley_47 1118
344 Berkeley_96 1118
345 Czernik_39 1118
346 ESO_371_25 1118
347 King_9 1118
348 Mon_OB1_D 1118
349 Dias_1 1118
350 FSR_0167 1118
351 FSR_0385 1118
352 NGC_743 1118
353 Ruprecht_107 1118
354 Ruprecht_16 1118
355 Stock_21 1118
356 ASCC_22 1118
357 Berkeley_4 1118
358 ESO_134_12 1118
359 FSR_0728 1118
360 Juchert_1 1118
361 Pismis_22 1118
362 Pismis_Moreno_1 1118
363 Ruprecht_4 1118
364 Berkeley_28 1118
365 Berkeley_95 1118
366 Czernik_9 1118
367 ESO_311_21 1118
368 ESO_312_03 1118
369 NGC_2343 1118
370 Ruprecht_34 1118
371 BH_132 1118
372 Berkeley_19 1118
373 FSR_0866 1118
374 Haffner_21 1118
375 NGC_4439 1118
376 Ruprecht_47 1118
377 SAI_94 1118
378 Teutsch_28 1118
379 Berkeley_52 1118
380 FSR_0735 1118
381 NGC_146 1118
382 Pismis_1 1118
383 Skiff_J1942+38.6 1118
384 Archinal_1 1118
385 Basel_18 1118
386 Berkeley_77 1118
387 Danks_2 1118
388 FSR_1211 1118
389 Kronberger_79 1118
390 NGC_7063 1118
391 Ruprecht_100 1118
392 Teutsch_126 1118
393 Trumpler_35 1118
394 ASCC_73 1118
395 BH_118 1118
396 BH_72 1118
397 Biurakan_2 1118
398 FSR_0941 1118
399 FSR_1085 1118
400 Haffner_26 1118
401 King_18 1118
402 Loden_1194 1118
403 NGC_7067 1118
404 Ruprecht_42 1118
405 ASCC_90 1118
406 Collinder_185 1118
407 Juchert_3 1118
408 SAI_91 1118
409 Trumpler_1 1118
410 ASCC_110 1118
411 Berkeley_27 1118
412 Collinder_338 1118
413 DBSB_7 1118
414 FSR_0974 1118
415 IC_2948 1118
416 NGC_6322 1118
417 NGC_6846 1118
418 Ruprecht_41 1118
419 Stock_4 1118
420 ASCC_10 1118
421 ASCC_99 1118
422 Berkeley_72 1118
423 Bochum_11 1118
424 Danks_1 1118
425 Haffner_18 1118
426 Koposov_10 1118
427 Lynga_4 1118
428 Ruprecht_36 1118
429 Ruprecht_94 1118
430 ASCC_128 1118
431 Collinder_269 1118
432 ESO_130_06 1118
433 NGC_6396 1118
434 Alessi_10 1118
435 Bochum_13 1118
436 Haffner_4 1118
437 King_12 1118
438 NGC_5269 1118
439 vdBergh_1 1118
440 Alessi_19 1118
441 FSR_0534 1118
442 King_15 1118
443 NGC_2183 1118
444 Teutsch_10 1118
445 ASCC_101 1118
446 NGC_1901 1118
447 Ruprecht_84 1118
448 BH_84 1118
449 Juchert_Saloran_1 1118
450 Loden_372 1118
451 Haffner_23 1118
452 NGC_637 1118
453 NGC_6520 1118
454 NGC_7296 1118
455 Pismis_23 1118
456 Ruprecht_48 1118
457 SAI_81 1118
458 Berkeley_71 1118
459 Kronberger_4 1118
460 NGC_6031 1118
461 NGC_6561 1118
462 Ruprecht_127 1118
463 Teutsch_144 1118
464 BDSB96 1118
465 Berkeley_6 1118
466 IC_4996 1118
467 Lynga_6 1118
468 NGC_2374 1118
469 SAI_113 1118
470 vdBergh_80 1118
471 Alessi_60 1118
472 BH_55 1118
473 NGC_136 1118
474 NGC_2129 1118
475 NGC_5749 1118
476 BH_85 1118
477 FSR_0166 1118
478 FSR_0306 1118
479 FSR_1591 1118
480 Ruprecht_26 1118
481 Teutsch_145 1118
482 Aveni_Hunter_1 1118
483 BH_144 1118
484 King_20 1118
485 Ruprecht_44 1118
486 FSR_1342 1118
487 Koposov_36 1118
488 NGC_744 1118
489 Ruprecht_161 1118
490 Ruprecht_37 1118
491 Teutsch_85 1118
492 Basel_11b 1118
493 Haffner_7 1118
494 Kronberger_52 1118
495 Trumpler_11 1118
496 FSR_1252 1118
497 FSR_1521 1118
498 Juchert_20 1118
499 King_4 1118
500 NGC_6250 1118
501 Berkeley_80 1118
502 NGC_6716 1118
503 SAI_109 1118
504 FSR_0165 1118
505 FSR_1180 1118
506 FSR_1716 1118
507 ASCC_9 1118
508 Czernik_31 1118
509 FSR_0282 1118
510 Basel_1 1118
511 Bica_3 1118
512 Ruprecht_167 1118
513 Stock_23 1118
514 Teutsch_51 1118
515 Berkeley_49 1118
516 Berkeley_90 1118
517 NGC_4463 1118
518 Platais_3 1118
519 SAI_86 1118
520 Teutsch_2 1118
521 Teutsch_49 1118
522 Czernik_19 1118
523 Dias_5 1118
524 Hogg_15 1118
525 NGC_2401 1118
526 Ruprecht_126 1118
527 Ruprecht_54 1118
528 Berkeley_11 1118
529 Czernik_2 1118
530 FSR_0716 1118
531 NGC_1348 1118
532 NGC_2358 1118
533 NGC_2567 1118
534 NGC_5593 1118
535 Pismis_5 1118
536 ASCC_71 1118
537 Berkeley_2 1118
538 Berkeley_97 1118
539 NGC_2972 1118
540 NGC_7261 1118
541 Stock_24 1118
542 BH_23 1118
543 Berkeley_99 1118
544 Czernik_30 1118
545 King_26 1118
546 Ruprecht_174 1118
547 Trumpler_26 1118
548 Berkeley_54 1118
549 FSR_0198 1118
550 NGC_2925 1118
551 Andrews_Lindsay_5 1118
552 FSR_1407 1118
553 FSR_1663 1118
554 Ruprecht_170 1118
555 Ruprecht_172 1118
556 Teutsch_22 1118
557 Teutsch_74 1118
558 NGC_2659 1118
559 NGC_2866 1118
560 Ruprecht_93 1118
561 Stephenson_1 1118
562 Collinder_132 1118
563 IC_1590 1118
564 King_8 1118
565 NGC_3680 1118
566 Ruprecht_117 1118
567 Skiff_J0507+30.8 1118
568 FSR_1402 1118
569 L_1641S 1118
570 NGC_1883 1118
571 Berkeley_51 1118
572 Collinder_205 1118
573 Czernik_13 1118
574 ESO_589_26 1118
575 NGC_2184 1118
576 NGC_2311 1118
577 NGC_3033 1118
578 Ruprecht_78 1118
579 Berkeley_35 1118
580 FSR_1083 1118
581 Ruprecht_83 1118
582 Basel_11a 1118
583 FSR_1750 1118
584 NGC_2533 1118
585 NGC_6866 1118
586 Berkeley_23 1118
587 Berkeley_30 1118
588 Collinder_292 1118
589 FSR_0953 1118
590 NGC_6357 1118
591 Trumpler_21 1118
592 ASCC_124 1118
593 Berkeley_45 1118
594 Haffner_16 1118
595 NGC_3105 1118
596 Ruprecht_143 1118
597 King_23 1118
598 Ruprecht_63 1118
599 Berkeley_7 1118
600 FSR_0275 1118
601 IC_5146 1118
602 BH_37 1118
603 Berkeley_60 1118
604 King_14 1118
605 NGC_5138 1118
606 NGC_6631 1118
607 Ruprecht_82 1118
608 Stock_12 1118
609 ASCC_13 1118
610 BH_211 1118
611 FSR_1723 1118
612 NGC_2186 1118
613 NGC_433 1118
614 Ruprecht_98 1118
615 SAI_24 1118
616 Haffner_8 1118
617 Roslund_3 1118
618 Ruprecht_119 1118
619 BH_200 1118
620 Berkeley_69 1118
621 Czernik_40 1118
622 ESO_368_11 1118
623 NGC_5288 1118
624 NGC_7062 1118
625 Pismis_21 1118
626 Pismis_4 1118
627 Berkeley_12 1118
628 Collinder_106 1118
629 ASCC_112 1118
630 Berkeley_62 1118
631 Haffner_17 1118
632 Stock_5 1118
633 King_21 1118
634 Pismis_20 1118
635 Ruprecht_96 1118
636 ESO_130_08 1118
637 Collinder_74 1118
638 NGC_2259 1118
639 Ruprecht_134 1118
640 Trumpler_7 1118
641 ASCC_85 1118
642 Czernik_23 1118
643 NGC_3255 1118
644 NGC_7039 1118
645 Teutsch_80 1118
646 Berkeley_55 1118
647 NGC_3572 1118
648 NGC_7380 1118
649 Pismis_7 1118
650 Czernik_24 1118
651 NGC_2304 1118
652 NGC_3228 1118
653 NGC_3330 1118
654 Ruprecht_138 1118
655 ASCC_127 1118
656 BH_56 1118
657 NGC_1857 1118
658 Ruprecht_115 1118
659 SAI_118 1118
660 Czernik_25 1118
661 NGC_6268 1118
662 NGC_6830 1118
663 Alessi_20 1118
664 Berkeley_37 1118
665 Collinder_258 1118
666 FSR_1253 1118
667 Berkeley_78 1118
668 NGC_2251 1118
669 ASCC_105 1118
670 ASCC_41 1118
671 FSR_0496 1118
672 IC_2581 1118
673 NGC_1605 1118
674 NGC_2453 1118
675 Ruprecht_23 1118
676 King_2 1118
677 NGC_2215 1118
678 NGC_2482 1118
679 Platais_9 1118
680 Ruprecht_27 1118
681 Ruprecht_45 1118
682 ASCC_23 1118
683 ASCC_79 1118
684 BH_217 1118
685 BH_222 1118
686 Ruprecht_1 1118
687 Berkeley_31 1118
688 Ruprecht_176 1118
689 ASCC_21 1118
690 Berkeley_87 1118
691 NGC_2455 1118
692 Teutsch_156 1118
693 Pismis_15 1118
694 ASCC_6 1118
695 Collinder_307 1118
696 ESO_130_13 1118
697 Ruprecht_28 1118
698 Trumpler_9 1118
699 Czernik_21 1118
700 NGC_366 1118
701 Roslund_2 1118
702 ESO_211_03 1118
703 NGC_2448 1118
704 NGC_6827 1118
705 Ruprecht_60 1118
706 IC_1805 1118
707 NGC_2910 1118
708 ASCC_58 1118
709 Alessi_21 1118
710 Collinder_220 1118
711 NGC_6997 1118
712 Ruprecht_85 1118
713 Berkeley_15 1118
714 DC_5 1118
715 NGC_1545 1118
716 Haffner_9 1118
717 Collinder_268 1118
718 Koposov_12 1118
719 NGC_1708 1118
720 NGC_6425 1118
721 Ruprecht_66 1118
722 Stock_7 1118
723 Berkeley_58 1118
724 NGC_2318 1118
725 Trumpler_28 1118
726 ASCC_88 1118
727 Collinder_95 1118
728 FSR_0124 1118
729 FSR_1586 1118
730 IC_348 1118
731 Stock_10 1118
732 Alessi_62 1118
733 BH_90 1118
734 NGC_6704 1118
735 Ruprecht_111 1118
736 Ruprecht_58 1118
737 FSR_1378 1118
738 King_19 1118
739 NGC_1582 1118
740 NGC_7423 1118
741 BH_202 1118
742 Berkeley_33 1118
743 NGC_381 1118
744 ASCC_114 1118
745 ASCC_77 1118
746 Collinder_140 1118
747 Roslund_7 1118
748 Berkeley_9 1118
749 NGC_2587 1118
750 NGC_581 1118
751 NGC_5460 1118
752 NGC_1502 1118
753 NGC_2414 1118
754 NGC_7788 1118
755 Trumpler_15 1118
756 Trumpler_16 1118
757 Berkeley_13 1118
758 NGC_659 1118
759 Trumpler_30 1118
760 ASCC_111 1118
761 Lynga_2 1118
762 NGC_2254 1118
763 NGC_2849 1118
764 Dolidze_5 1118
765 NGC_6823 1118
766 Collinder_107 1118
767 NGC_6910 1118
768 Ruprecht_164 1118
769 NGC_957 1118
770 NGC_7092 1118
771 ASCC_12 1118
772 BH_221 1118
773 Berkeley_67 1118
774 NGC_2192 1118
775 FSR_0942 1118
776 Harvard_10 1118
777 NGC_2671 1118
778 Roslund_5 1118
779 NGC_2362 1118
780 NGC_2546 1118
781 NGC_2571 1118
782 NGC_6568 1118
783 vdBergh_130 1118
784 Berkeley_98 1118
785 Haffner_11 1118
786 King_25 1118
787 SAI_116 1118
788 NGC_1893 1118
789 NGC_2286 1118
790 NGC_6735 1118
791 NGC_2264 1118
792 Berkeley_81 1118
793 IC_4665 1118
794 NGC_4052 1118
795 NGC_7031 1118
796 Haffner_10 1118
797 NGC_436 1118
798 NGC_5715 1118
799 Czernik_32 1118
800 Ruprecht_147 1118
801 Tombaugh_4 1118
802 Haffner_15 1118
803 NGC_1778 1118
804 SAI_132 1118
805 Alessi_6 1118
806 Alessi_2 1118
807 Berkeley_36 1118
808 NGC_2635 1118
809 Trumpler_12 1118
810 Alessi_3 1118
811 Collinder_350 1118
812 NGC_2428 1118
813 Ruprecht_79 1118
814 Berkeley_70 1118
815 Teutsch_84 1118
816 Trumpler_2 1118
817 NGC_7128 1118
818 Berkeley_10 1118
819 Lynga_5 1118
820 NGC_6216 1118
821 Collinder_394 1118
822 NGC_6318 1118
823 Stock_1 1118
824 Haffner_22 1118
825 NGC_3590 1118
826 Trumpler_29 1118
827 Westerlund_2 1118
828 NGC_6633 1118
829 ASCC_19 1118
830 Trumpler_32 1118
831 Haffner_6 1118
832 NGC_6793 1118
833 Haffner_5 1118
834 NGC_2432 1118
835 NGC_6583 1118
836 Trumpler_22 1118
837 Czernik_29 1118
838 NGC_103 1118
839 vdBergh_92 1118
840 Alessi_9 1118
841 Berkeley_89 1118
842 Czernik_38 1118
843 FSR_0336 1118
844 Melotte_72 1118
845 NGC_2335 1118
846 Haffner_14 1118
847 ASCC_113 1118
848 Trumpler_17 1118
849 Ruprecht_128 1118
850 NGC_2232 1118
851 BH_87 1118
852 Mamajek_4 1118
853 IC_1369 1118
854 NGC_7790 1118
855 Pismis_12 1118
856 NGC_6152 1118
857 NGC_6400 1118
858 Berkeley_14 1118
859 NGC_6694 1118
860 NGC_5168 1118
861 Collinder_277 1118
862 Pismis_2 1118
863 Juchert_13 1118
864 NGC_4852 1118
865 NGC_7235 1118
866 BH_164 1118
867 Berkeley_44 1118
868 NGC_2670 1118
869 Haffner_13 1118
870 NGC_4349 1118
871 Platais_8 1118
872 Tombaugh_1 1118
873 Ruprecht_91 1118
874 Collinder_115 1118
875 NGC_1193 1118
876 NGC_1798 1118
877 Berkeley_43 1118
878 NGC_2353 1118
879 NGC_6531 1118
880 NGC_2669 1118
881 Trumpler_13 1118
882 Trumpler_3 1118
883 BH_121 1118
884 NGC_6204 1118
885 IC_2391 1118
886 NGC_2309 1118
887 ASCC_16 1118
888 NGC_5999 1118
889 King_10 1118
890 Dias_6 1118
891 Czernik_37 1118
892 Ruprecht_145 1118
893 Ruprecht_18 1118
894 ASCC_108 1118
895 NGC_6728 1118
896 FSR_0342 1118
897 NGC_4337 1118
898 NGC_2547 1118
899 NGC_6451 1118
900 Czernik_41 1118
901 King_6 1118
902 Cep_OB5 1118
903 NGC_6664 1118
904 NGC_1662 1118
905 NGC_609 1118
906 Berkeley_59 1118
907 NGC_752 1118
908 NGC_6167 1118
909 Collinder_197 1118
910 Berkeley_24 1118
911 Berkeley_68 1118
912 NGC_2266 1118
913 Roslund_6 1118
914 FSR_0088 1118
915 NGC_6087 1118
916 Berkeley_8 1118
917 NGC_2262 1118
918 NGC_2509 1118
919 NGC_5662 1118
920 NGC_2425 1118
921 ASCC_32 1118
922 NGC_2383 1118
923 NGC_6834 1118
924 Berkeley_17 1118
925 Pismis_18 1118
926 Tombaugh_2 1118
927 NGC_6756 1118
928 NGC_7245 1118
929 Cep_OB3 1118
930 NGC_6383 1118
931 King_11 1118
932 NGC_2818 1118
933 NGC_6709 1118
934 ASCC_11 1118
935 NGC_2354 1118
936 NGC_6416 1118
937 Hogg_4 1118
938 IC_1434 1118
939 NGC_6404 1118
940 NGC_6755 1118
941 NGC_2489 1118
942 King_5 1118
943 NGC_3960 1118
944 King_13 1118
945 NGC_2355 1118
946 Collinder_272 1118
947 NGC_1907 1118
948 IC_2395 1118
949 NGC_2451B 1118
950 NGC_1960 1118
951 Alessi_5 1118
952 NGC_6811 1118
953 NGC_2324 1118
954 Ruprecht_101 1118
955 IC_2602 1118
956 NGC_7209 1118
957 Alessi_12 1118
958 NGC_3603 1118
959 NGC_7243 1118
960 NGC_7082 1118
961 FSR_0133 1118
962 Per_OB2 1118
963 Berkeley_18 1118
964 NGC_4609 1118
965 Trumpler_14 1118
966 NGC_7510 1118
967 NGC_6025 1118
968 NGC_1664 1118
969 Collinder_359 1118
970 NGC_1528 1118
971 NGC_7419 1118
972 NGC_2451A 1118
973 NGC_2627 1118
974 NGC_6005 1118
975 NGC_6208 1118
976 NGC_1513 1118
977 Stock_8 1118
978 Collinder_135 1118
979 Ruprecht_68 1118
980 NGC_2527 1118
981 Westerlund_1 1118
982 NGC_1342 1118
983 NGC_654 1118
984 NGC_4103 1118
985 NGC_6645 1118
986 NGC_3293 1118
987 King_7 1118
988 Skiff_J0058+68.4 1118
989 Blanco_1 1118
990 IC_1848 1118
991 Berkeley_85 1118
992 NGC_6611 1118
993 NGC_6802 1118
994 BH_99 1118
995 NGC_2658 1118
996 NGC_129 1118
997 NGC_1027 1118
998 Ruprecht_121 1118
999 NGC_5316 1118
1000 NGC_7142 1118
1001 Berkeley_32 1118
1002 NGC_6253 1118
1003 Harvard_16 1118
1004 NGC_2281 1118
1005 Ruprecht_112 1118
1006 NGC_2660 1118
1007 Trumpler_23 1118
1008 NGC_2423 1118
1009 NGC_7762 1118
1010 Collinder_463 1118
1011 NGC_2421 1118
1012 NGC_2236 1118
1013 NGC_3496 1118
1014 IC_2488 1118
1015 NGC_2420 1118
1016 BH_140 1118
1017 NGC_2422 1118
1018 NGC_5381 1118
1019 NGC_5925 1118
1020 Lynga_9 1118
1021 Pismis_19 1118
1022 NGC_2301 1118
1023 IC_1396 1118
1024 NGC_1817 1118
1025 NGC_6193 1118
1026 Tombaugh_5 1118
1027 NGC_1912 1118
1028 NGC_2345 1118
1029 Trumpler_10 1118
1030 NGC_2548 1118
1031 NGC_884 1118
1032 IC_4756 1118
1033 NGC_6603 1118
1034 IC_1311 1118
1035 IC_361 1118
1036 Melotte_105 1118
1037 NGC_5823 1118
1038 NGC_6281 1118
1039 NGC_2243 1118
1040 IC_4725 1118
1041 NGC_2539 1118
1042 Melotte_101 1118
1043 NGC_6242 1118
1044 NGC_6192 1118
1045 NGC_559 1118
1046 Berkeley_39 1118
1047 NGC_2439 1118
1048 NGC_6649 1118
1049 NGC_2204 1118
1050 NGC_1039 1118
1051 Cyg_OB2 1118
1052 NGC_6405 1118
1053 Melotte_71 1118
1054 NGC_1245 1118
1055 NGC_6940 1118
1056 NGC_457 1118
1057 NGC_6871 1118
1058 NGC_4815 1118
1059 Berkeley_53 1118
1060 NGC_1647 1118
1061 NGC_7086 1118
1062 NGC_2244 1118
1063 NGC_663 1118
1064 NGC_5617 1118
1065 Trumpler_19 1118
1066 NGC_2287 1118
1067 NGC_4755 1118
1068 NGC_6231 1118
1069 NGC_6939 1118
1070 NGC_3766 1118
1071 NGC_5822 1118
1072 Collinder_69 1118
1073 NGC_7044 1118
1074 Trumpler_25 1118
1075 King_1 1118
1076 NGC_2682 1118
1077 NGC_2360 1118
1078 NGC_2632 1118
1079 NGC_2112 1118
1080 NGC_869 1118
1081 IC_166 1118
1082 Ruprecht_171 1118
1083 NGC_2447 1118
1084 Melotte_20 1118
1085 NGC_6494 1118
1086 NGC_2516 1118
1087 NGC_6134 1118
1088 NGC_2194 1118
1089 Melotte_66 1118
1090 NGC_2141 1118
1091 IC_4651 1118
1092 NGC_188 1118
1093 NGC_2323 1118
1094 Trumpler_20 1118
1095 IC_2714 1118
1096 NGC_6475 1118
1097 Collinder_110 1118
1098 NGC_6259 1118
1099 Melotte_22 1118
1100 NGC_6067 1118
1101 NGC_7654 1118
1102 Pismis_3 1118
1103 Stock_2 1118
1104 NGC_3114 1118
1105 NGC_2168 1118
1106 NGC_6124 1118
1107 NGC_2158 1118
1108 NGC_6705 1118
1109 NGC_2506 1118
1110 NGC_6819 1118
1111 NGC_2099 1118
1112 NGC_2437 1118
1113 Collinder_261 1118
1114 NGC_3532 1118
1115 NGC_2477 1118
1116 Trumpler_5 1118
1117 NGC_7789 1118
```python
seed = 1
create_gdr2mock_mag_limited_survey_from_nbody(nbody_filename = filename,nside = 512,
outputDir = folder_cat + "_%d" %(seed),
use_previous = False, delete_ebf = True,
fSample = 1, make_likelihood_asessment=False, seed = seed, popid=11)
```
/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)
/home/rybizki/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)
/home/rybizki/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
from numpy.core.umath_tests import inner1d
../output/Clusters_1/ existed and was recreated
Galaxia spawns catalogue
output: b'Galaxia-v0.81\nCODEDATAPATH=/home/rybizki/Programme/GalaxiaData/\nReading Parameter file- ../output/Clusters_1/nbody.log\n--------------------------------------------------------\noutputFile nbody \nmodelFile Model/population_parameters_BGM_update.ebf\ncodeDataDir /home/rybizki/Programme/GalaxiaData\noutputDir ../output/Clusters_1 \nphotoSys parsec1/GAIADR3 \nmagcolorNames gaia_g,gaia_bpft-gaia_rp\nappMagLimits[0] -1000.000000 \nappMagLimits[1] 20.700000 \nabsMagLimits[0] -1000.000000 \nabsMagLimits[1] 1000.000000 \ncolorLimits[0] -1000.000000 \ncolorLimits[1] 1000.000000 \ngeometryOption 0 \nlongitude 0.000000 \nlatitude 90.000000 \nsurveyArea 1000.000000 \nfSample 1.000000 \npopID -1 \nwarpFlareOn 1 \nseed 1 \nr_max 1000.000000 \nstarType 0 \nphotoError 0 \n--------------------------------------------------------\nReading Halo Sat File=/home/rybizki/Programme/GalaxiaData/nbody1/filenames/cluster_list.txt\nnbody1/cluster/cluster_listLynga_15.ebf 0\nnbody1/cluster/cluster_listFSR_1051.ebf 0\nnbody1/cluster/cluster_listFSR_1397.ebf 0\nnbody1/cluster/cluster_listRuprecht_43.ebf 0\nnbody1/cluster/cluster_listSAI_72.ebf 0\nnbody1/cluster/cluster_listESO_313_03.ebf 0\nnbody1/cluster/cluster_listCzernik_26.ebf 0\nnbody1/cluster/cluster_listBerkeley_14A.ebf 0\nnbody1/cluster/cluster_listStock_17.ebf 0\nnbody1/cluster/cluster_listDBSB_21.ebf 0\nnbody1/cluster/cluster_listFSR_1025.ebf 0\nnbody1/cluster/cluster_listFSR_1580.ebf 0\nnbody1/cluster/cluster_listRuprecht_24.ebf 0\nnbody1/cluster/cluster_listTeutsch_42.ebf 0\nnbody1/cluster/cluster_listBerkeley_29.ebf 0\nnbody1/cluster/cluster_listFSR_0401.ebf 0\nnbody1/cluster/cluster_listFSR_0975.ebf 0\nnbody1/cluster/cluster_listFSR_1419.ebf 0\nnbody1/cluster/cluster_listKoposov_43.ebf 0\nnbody1/cluster/cluster_listFSR_1172.ebf 0\nnbody1/cluster/cluster_listIvanov_8.ebf 0\nnbody1/cluster/cluster_listAlessi_15.ebf 0\nnbody1/cluster/cluster_listAlessi_59.ebf 0\nnbody1/cluster/cluster_listBasel_10.ebf 0\nnbody1/cluster/cluster_listESO_559_13.ebf 0\nnbody1/cluster/cluster_listFSR_1460.ebf 0\nnbody1/cluster/cluster_listRuprecht_32.ebf 0\nnbody1/cluster/cluster_listFSR_0284.ebf 0\nnbody1/cluster/cluster_listFSR_0524.ebf 0\nnbody1/cluster/cluster_listFSR_1335.ebf 0\nnbody1/cluster/cluster_listHogg_10.ebf 0\nnbody1/cluster/cluster_listNGC_1624.ebf 0\nnbody1/cluster/cluster_listSAI_47.ebf 0\nnbody1/cluster/cluster_listTeutsch_13.ebf 0\nnbody1/cluster/cluster_listTeutsch_52.ebf 0\nnbody1/cluster/cluster_listTurner_3.ebf 0\nnbody1/cluster/cluster_listKronberger_54.ebf 0\nnbody1/cluster/cluster_listStock_13.ebf 0\nnbody1/cluster/cluster_listTeutsch_50.ebf 0\nnbody1/cluster/cluster_listCzernik_43.ebf 0\nnbody1/cluster/cluster_listFSR_1171.ebf 0\nnbody1/cluster/cluster_listGraham_1.ebf 0\nnbody1/cluster/cluster_listMamajek_1.ebf 0\nnbody1/cluster/cluster_listSchuster_1.ebf 0\nnbody1/cluster/cluster_listTeutsch_27.ebf 0\nnbody1/cluster/cluster_listFSR_1032.ebf 0\nnbody1/cluster/cluster_listIvanov_4.ebf 0\nnbody1/cluster/cluster_listNGC_1444.ebf 0\nnbody1/cluster/cluster_listPlatais_10.ebf 0\nnbody1/cluster/cluster_listTeutsch_31.ebf 0\nnbody1/cluster/cluster_listASCC_66.ebf 0\nnbody1/cluster/cluster_listBerkeley_102.ebf 0\nnbody1/cluster/cluster_listESO_393_15.ebf 0\nnbody1/cluster/cluster_listFSR_0977.ebf 0\nnbody1/cluster/cluster_listFSR_1352.ebf 0\nnbody1/cluster/cluster_listRuprecht_77.ebf 0\nnbody1/cluster/cluster_listDB2001_22.ebf 0\nnbody1/cluster/cluster_listPatchick_90.ebf 0\nnbody1/cluster/cluster_listBerkeley_25.ebf 0\nnbody1/cluster/cluster_listHogg_18.ebf 0\nnbody1/cluster/cluster_listSkiff_J0458+43.0.ebf 0\nnbody1/cluster/cluster_listTeutsch_11.ebf 0\nnbody1/cluster/cluster_listDBSB_6.ebf 0\nnbody1/cluster/cluster_listFSR_1170.ebf 0\nnbody1/cluster/cluster_listJuchert_19.ebf 0\nnbody1/cluster/cluster_listLynga_14.ebf 0\nnbody1/cluster/cluster_listPatchick_75.ebf 0\nnbody1/cluster/cluster_listSkiff_J0619+18.5.ebf 0\nnbody1/cluster/cluster_listStock_18.ebf 0\nnbody1/cluster/cluster_listAlessi_17.ebf 0\nnbody1/cluster/cluster_listAlessi_18.ebf 0\nnbody1/cluster/cluster_listBerkeley_82.ebf 0\nnbody1/cluster/cluster_listBochum_4.ebf 0\nnbody1/cluster/cluster_listDBSB_104.ebf 0\nnbody1/cluster/cluster_listFSR_0158.ebf 0\nnbody1/cluster/cluster_listFSR_0465.ebf 0\nnbody1/cluster/cluster_listMarkarian_38.ebf 0\nnbody1/cluster/cluster_listCzernik_6.ebf 0\nnbody1/cluster/cluster_listDBSB_43.ebf 0\nnbody1/cluster/cluster_listESO_211_09.ebf 0\nnbody1/cluster/cluster_listFSR_1297.ebf 0\nnbody1/cluster/cluster_listTeutsch_66.ebf 0\nnbody1/cluster/cluster_listTeutsch_8.ebf 0\nnbody1/cluster/cluster_listToepler_1.ebf 0\nnbody1/cluster/cluster_listTurner_5.ebf 0\nnbody1/cluster/cluster_listBH_111.ebf 0\nnbody1/cluster/cluster_listKronberger_57.ebf 0\nnbody1/cluster/cluster_listTeutsch_125.ebf 0\nnbody1/cluster/cluster_listESO_226_06.ebf 0\nnbody1/cluster/cluster_listFSR_1363.ebf 0\nnbody1/cluster/cluster_listRuprecht_10.ebf 0\nnbody1/cluster/cluster_listBasel_17.ebf 0\nnbody1/cluster/cluster_listBerkeley_5.ebf 0\nnbody1/cluster/cluster_listBerkeley_86.ebf 0\nnbody1/cluster/cluster_listDBSB_60.ebf 0\nnbody1/cluster/cluster_listDC_8.ebf 0\nnbody1/cluster/cluster_listDolidze_11.ebf 0\nnbody1/cluster/cluster_listFSR_0833.ebf 0\nnbody1/cluster/cluster_listFSR_1117.ebf 0\nnbody1/cluster/cluster_listTeutsch_30.ebf 0\nnbody1/cluster/cluster_listTeutsch_54.ebf 0\nnbody1/cluster/cluster_listBDSB91.ebf 0\nnbody1/cluster/cluster_listBochum_3.ebf 0\nnbody1/cluster/cluster_listCollinder_469.ebf 0\nnbody1/cluster/cluster_listJuchert_18.ebf 0\nnbody1/cluster/cluster_listKronberger_85.ebf 0\nnbody1/cluster/cluster_listNGC_1724.ebf 0\nnbody1/cluster/cluster_listPfleiderer_3.ebf 0\nnbody1/cluster/cluster_listSAI_149.ebf 0\nnbody1/cluster/cluster_listArp_Madore_2.ebf 0\nnbody1/cluster/cluster_listBDSB30.ebf 0\nnbody1/cluster/cluster_listBDSB93.ebf 0\nnbody1/cluster/cluster_listBasel_8.ebf 0\nnbody1/cluster/cluster_listFSR_0238.ebf 0\nnbody1/cluster/cluster_listFSR_0683.ebf 0\nnbody1/cluster/cluster_listFSR_0905.ebf 0\nnbody1/cluster/cluster_listHavlen_Moffat_1.ebf 0\nnbody1/cluster/cluster_listLDN_988e.ebf 0\nnbody1/cluster/cluster_listLynga_1.ebf 0\nnbody1/cluster/cluster_listNGC_2580.ebf 0\nnbody1/cluster/cluster_listNGC_7058.ebf 0\nnbody1/cluster/cluster_listPismis_17.ebf 0\nnbody1/cluster/cluster_listRuprecht_25.ebf 0\nnbody1/cluster/cluster_listSkiff_J2330+60.2.ebf 0\nnbody1/cluster/cluster_listFSR_0536.ebf 0\nnbody1/cluster/cluster_listPatchick_94.ebf 0\nnbody1/cluster/cluster_listRuprecht_61.ebf 0\nnbody1/cluster/cluster_listSAI_17.ebf 0\nnbody1/cluster/cluster_listTeutsch_7.ebf 0\nnbody1/cluster/cluster_listvdBergh_85.ebf 0\nnbody1/cluster/cluster_listAntalova_2.ebf 0\nnbody1/cluster/cluster_listFSR_0826.ebf 0\nnbody1/cluster/cluster_listFSR_0852.ebf 0\nnbody1/cluster/cluster_listNGC_7024.ebf 0\nnbody1/cluster/cluster_listBH_151.ebf 0\nnbody1/cluster/cluster_listDolidze_3.ebf 0\nnbody1/cluster/cluster_listESO_166_04.ebf 0\nnbody1/cluster/cluster_listFSR_0430.ebf 0\nnbody1/cluster/cluster_listFSR_0968.ebf 0\nnbody1/cluster/cluster_listFSR_1125.ebf 0\nnbody1/cluster/cluster_listFSR_1484.ebf 0\nnbody1/cluster/cluster_listNGC_225.ebf 0\nnbody1/cluster/cluster_listNGC_6800.ebf 0\nnbody1/cluster/cluster_listNGC_7129.ebf 0\nnbody1/cluster/cluster_listASCC_97.ebf 0\nnbody1/cluster/cluster_listBerkeley_20.ebf 0\nnbody1/cluster/cluster_listBerkeley_34.ebf 0\nnbody1/cluster/cluster_listESO_368_14.ebf 0\nnbody1/cluster/cluster_listFSR_0667.ebf 0\nnbody1/cluster/cluster_listFSR_1183.ebf 0\nnbody1/cluster/cluster_listFeibelman_1.ebf 0\nnbody1/cluster/cluster_listRuprecht_29.ebf 0\nnbody1/cluster/cluster_listSher_1.ebf 0\nnbody1/cluster/cluster_listTeutsch_23.ebf 0\nnbody1/cluster/cluster_listBerkeley_83.ebf 0\nnbody1/cluster/cluster_listCzernik_12.ebf 0\nnbody1/cluster/cluster_listDolidze_32.ebf 0\nnbody1/cluster/cluster_listKronberger_1.ebf 0\nnbody1/cluster/cluster_listLynga_3.ebf 0\nnbody1/cluster/cluster_listSAI_25.ebf 0\nnbody1/cluster/cluster_listASCC_115.ebf 0\nnbody1/cluster/cluster_listAlessi_53.ebf 0\nnbody1/cluster/cluster_listBerkeley_66.ebf 0\nnbody1/cluster/cluster_listBerkeley_92.ebf 0\nnbody1/cluster/cluster_listCzernik_1.ebf 0\nnbody1/cluster/cluster_listESO_312_04.ebf 0\nnbody1/cluster/cluster_listFSR_1399.ebf 0\nnbody1/cluster/cluster_listFSR_1452.ebf 0\nnbody1/cluster/cluster_listFSR_1509.ebf 0\nnbody1/cluster/cluster_listIC_2157.ebf 0\nnbody1/cluster/cluster_listJuchert_9.ebf 0\nnbody1/cluster/cluster_listBerkeley_103.ebf 0\nnbody1/cluster/cluster_listCzernik_8.ebf 0\nnbody1/cluster/cluster_listMuzzio_1.ebf 0\nnbody1/cluster/cluster_listPismis_27.ebf 0\nnbody1/cluster/cluster_listTrumpler_34.ebf 0\nnbody1/cluster/cluster_listWaterloo_7.ebf 0\nnbody1/cluster/cluster_listBerkeley_91.ebf 0\nnbody1/cluster/cluster_listFSR_0542.ebf 0\nnbody1/cluster/cluster_listFSR_0883.ebf 0\nnbody1/cluster/cluster_listKronberger_80.ebf 0\nnbody1/cluster/cluster_listRuprecht_151.ebf 0\nnbody1/cluster/cluster_listCzernik_10.ebf 0\nnbody1/cluster/cluster_listCzernik_20.ebf 0\nnbody1/cluster/cluster_listDolidze_53.ebf 0\nnbody1/cluster/cluster_listFSR_0296.ebf 0\nnbody1/cluster/cluster_listNGC_2367.ebf 0\nnbody1/cluster/cluster_listRuprecht_76.ebf 0\nnbody1/cluster/cluster_listSaurer_2.ebf 0\nnbody1/cluster/cluster_listTrumpler_33.ebf 0\nnbody1/cluster/cluster_listBH_66.ebf 0\nnbody1/cluster/cluster_listBH_92.ebf 0\nnbody1/cluster/cluster_listDolidze_8.ebf 0\nnbody1/cluster/cluster_listBH_245.ebf 0\nnbody1/cluster/cluster_listFSR_0921.ebf 0\nnbody1/cluster/cluster_listFSR_1063.ebf 0\nnbody1/cluster/cluster_listFSR_1284.ebf 0\nnbody1/cluster/cluster_listKharchenko_1.ebf 0\nnbody1/cluster/cluster_listKronberger_84.ebf 0\nnbody1/cluster/cluster_listLoden_46.ebf 0\nnbody1/cluster/cluster_listRuprecht_108.ebf 0\nnbody1/cluster/cluster_listSAI_16.ebf 0\nnbody1/cluster/cluster_listTeutsch_103.ebf 0\nnbody1/cluster/cluster_listBerkeley_61.ebf 0\nnbody1/cluster/cluster_listBerkeley_65.ebf 0\nnbody1/cluster/cluster_listDBSB_101.ebf 0\nnbody1/cluster/cluster_listDBSB_3.ebf 0\nnbody1/cluster/cluster_listFSR_0553.ebf 0\nnbody1/cluster/cluster_listFSR_1260.ebf 0\nnbody1/cluster/cluster_listHaffner_3.ebf 0\nnbody1/cluster/cluster_listHogg_19.ebf 0\nnbody1/cluster/cluster_listKronberger_69.ebf 0\nnbody1/cluster/cluster_listMarkarian_50.ebf 0\nnbody1/cluster/cluster_listNGC_5606.ebf 0\nnbody1/cluster/cluster_listNGC_6178.ebf 0\nnbody1/cluster/cluster_listPatchick_3.ebf 0\nnbody1/cluster/cluster_listRuprecht_148.ebf 0\nnbody1/cluster/cluster_listSAI_14.ebf 0\nnbody1/cluster/cluster_listTeutsch_44.ebf 0\nnbody1/cluster/cluster_listASCC_67.ebf 0\nnbody1/cluster/cluster_listFSR_1150.ebf 0\nnbody1/cluster/cluster_listFSR_1212.ebf 0\nnbody1/cluster/cluster_listNGC_2588.ebf 0\nnbody1/cluster/cluster_listStock_14.ebf 0\nnbody1/cluster/cluster_listTurner_9.ebf 0\nnbody1/cluster/cluster_listFSR_0384.ebf 0\nnbody1/cluster/cluster_listFSR_0448.ebf 0\nnbody1/cluster/cluster_listKronberger_81.ebf 0\nnbody1/cluster/cluster_listNGC_7160.ebf 0\nnbody1/cluster/cluster_listPismis_11.ebf 0\nnbody1/cluster/cluster_listRuprecht_97.ebf 0\nnbody1/cluster/cluster_listAlessi_1.ebf 0\nnbody1/cluster/cluster_listAlessi_13.ebf 0\nnbody1/cluster/cluster_listCzernik_3.ebf 0\nnbody1/cluster/cluster_listCzernik_44.ebf 0\nnbody1/cluster/cluster_listRuprecht_50.ebf 0\nnbody1/cluster/cluster_listBH_54.ebf 0\nnbody1/cluster/cluster_listBerkeley_75.ebf 0\nnbody1/cluster/cluster_listCzernik_42.ebf 0\nnbody1/cluster/cluster_listFSR_0948.ebf 0\nnbody1/cluster/cluster_listKing_16.ebf 0\nnbody1/cluster/cluster_listNGC_6613.ebf 0\nnbody1/cluster/cluster_listRuprecht_71.ebf 0\nnbody1/cluster/cluster_listBH_78.ebf 0\nnbody1/cluster/cluster_listFSR_0172.ebf 0\nnbody1/cluster/cluster_listFSR_0357.ebf 0\nnbody1/cluster/cluster_listFSR_1530.ebf 0\nnbody1/cluster/cluster_listHaffner_19.ebf 0\nnbody1/cluster/cluster_listKing_17.ebf 0\nnbody1/cluster/cluster_listKoposov_53.ebf 0\nnbody1/cluster/cluster_listNGC_1333.ebf 0\nnbody1/cluster/cluster_listRoslund_4.ebf 0\nnbody1/cluster/cluster_listRuprecht_35.ebf 0\nnbody1/cluster/cluster_listSAI_108.ebf 0\nnbody1/cluster/cluster_listStock_20.ebf 0\nnbody1/cluster/cluster_listTeutsch_61.ebf 0\nnbody1/cluster/cluster_listASCC_87.ebf 0\nnbody1/cluster/cluster_listAuner_1.ebf 0\nnbody1/cluster/cluster_listBH_67.ebf 0\nnbody1/cluster/cluster_listBerkeley_73.ebf 0\nnbody1/cluster/cluster_listCollinder_419.ebf 0\nnbody1/cluster/cluster_listCzernik_18.ebf 0\nnbody1/cluster/cluster_listFSR_0811.ebf 0\nnbody1/cluster/cluster_listFSR_0935.ebf 0\nnbody1/cluster/cluster_listNGC_2269.ebf 0\nnbody1/cluster/cluster_listNGC_5764.ebf 0\nnbody1/cluster/cluster_listNGC_7226.ebf 0\nnbody1/cluster/cluster_listRuprecht_102.ebf 0\nnbody1/cluster/cluster_listRuprecht_19.ebf 0\nnbody1/cluster/cluster_listSAI_4.ebf 0\nnbody1/cluster/cluster_listBerkeley_101.ebf 0\nnbody1/cluster/cluster_listBerkeley_104.ebf 0\nnbody1/cluster/cluster_listBerkeley_21.ebf 0\nnbody1/cluster/cluster_listBerkeley_76.ebf 0\nnbody1/cluster/cluster_listBerkeley_94.ebf 0\nnbody1/cluster/cluster_listDBSB_100.ebf 0\nnbody1/cluster/cluster_listDolidze_16.ebf 0\nnbody1/cluster/cluster_listFSR_0537.ebf 0\nnbody1/cluster/cluster_listFSR_0551.ebf 0\nnbody1/cluster/cluster_listFSR_0923.ebf 0\nnbody1/cluster/cluster_listFSR_1435.ebf 0\nnbody1/cluster/cluster_listStock_16.ebf 0\nnbody1/cluster/cluster_listTeutsch_106.ebf 0\nnbody1/cluster/cluster_listvdBergh_83.ebf 0\nnbody1/cluster/cluster_listBH_19.ebf 0\nnbody1/cluster/cluster_listCollinder_271.ebf 0\nnbody1/cluster/cluster_listKoposov_63.ebf 0\nnbody1/cluster/cluster_listNegueruela_1.ebf 0\nnbody1/cluster/cluster_listRiddle_4.ebf 0\nnbody1/cluster/cluster_listASCC_29.ebf 0\nnbody1/cluster/cluster_listCzernik_16.ebf 0\nnbody1/cluster/cluster_listDias_2.ebf 0\nnbody1/cluster/cluster_listHaffner_20.ebf 0\nnbody1/cluster/cluster_listNGC_5281.ebf 0\nnbody1/cluster/cluster_listPismis_8.ebf 0\nnbody1/cluster/cluster_listRuprecht_105.ebf 0\nnbody1/cluster/cluster_listRuprecht_130.ebf 0\nnbody1/cluster/cluster_listASCC_123.ebf 0\nnbody1/cluster/cluster_listAlessi_8.ebf 0\nnbody1/cluster/cluster_listBH_150.ebf 0\nnbody1/cluster/cluster_listBasel_4.ebf 0\nnbody1/cluster/cluster_listESO_092_05.ebf 0\nnbody1/cluster/cluster_listHogg_17.ebf 0\nnbody1/cluster/cluster_listPismis_24.ebf 0\nnbody1/cluster/cluster_listRuprecht_75.ebf 0\nnbody1/cluster/cluster_listTrumpler_18.ebf 0\nnbody1/cluster/cluster_listBerkeley_63.ebf 0\nnbody1/cluster/cluster_listFSR_0195.ebf 0\nnbody1/cluster/cluster_listMayer_1.ebf 0\nnbody1/cluster/cluster_listNGC_1579.ebf 0\nnbody1/cluster/cluster_listRuprecht_135.ebf 0\nnbody1/cluster/cluster_listBerkeley_1.ebf 0\nnbody1/cluster/cluster_listCzernik_27.ebf 0\nnbody1/cluster/cluster_listFSR_0985.ebf 0\nnbody1/cluster/cluster_listFSR_1441.ebf 0\nnbody1/cluster/cluster_listFSR_1595.ebf 0\nnbody1/cluster/cluster_listHarvard_13.ebf 0\nnbody1/cluster/cluster_listNGC_1220.ebf 0\nnbody1/cluster/cluster_listNGC_189.ebf 0\nnbody1/cluster/cluster_listNGC_6469.ebf 0\nnbody1/cluster/cluster_listNGC_7281.ebf 0\nnbody1/cluster/cluster_listSkiff_J0614+12.9.ebf 0\nnbody1/cluster/cluster_listCzernik_14.ebf 0\nnbody1/cluster/cluster_listFSR_0398.ebf 0\nnbody1/cluster/cluster_listHogg_21.ebf 0\nnbody1/cluster/cluster_listNGC_1496.ebf 0\nnbody1/cluster/cluster_listRuprecht_144.ebf 0\nnbody1/cluster/cluster_listWaterloo_1.ebf 0\nnbody1/cluster/cluster_listASCC_107.ebf 0\nnbody1/cluster/cluster_listASCC_30.ebf 0\nnbody1/cluster/cluster_listBH_73.ebf 0\nnbody1/cluster/cluster_listESO_092_18.ebf 0\nnbody1/cluster/cluster_listFSR_1207.ebf 0\nnbody1/cluster/cluster_listFSR_1380.ebf 0\nnbody1/cluster/cluster_listBarkhatova_1.ebf 0\nnbody1/cluster/cluster_listFSR_0932.ebf 0\nnbody1/cluster/cluster_listFSR_1360.ebf 0\nnbody1/cluster/cluster_listNGC_2225.ebf 0\nnbody1/cluster/cluster_listNGC_2302.ebf 0\nnbody1/cluster/cluster_listNGC_6249.ebf 0\nnbody1/cluster/cluster_listRuprecht_33.ebf 0\nnbody1/cluster/cluster_listRuprecht_67.ebf 0\nnbody1/cluster/cluster_listTeutsch_14a.ebf 0\nnbody1/cluster/cluster_listBerkeley_47.ebf 0\nnbody1/cluster/cluster_listBerkeley_96.ebf 0\nnbody1/cluster/cluster_listCzernik_39.ebf 0\nnbody1/cluster/cluster_listESO_371_25.ebf 0\nnbody1/cluster/cluster_listKing_9.ebf 0\nnbody1/cluster/cluster_listMon_OB1_D.ebf 0\nnbody1/cluster/cluster_listDias_1.ebf 0\nnbody1/cluster/cluster_listFSR_0167.ebf 0\nnbody1/cluster/cluster_listFSR_0385.ebf 0\nnbody1/cluster/cluster_listNGC_743.ebf 0\nnbody1/cluster/cluster_listRuprecht_107.ebf 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0\nnbody1/cluster/cluster_listMelotte_66.ebf 0\nnbody1/cluster/cluster_listNGC_2141.ebf 0\nnbody1/cluster/cluster_listIC_4651.ebf 0\nnbody1/cluster/cluster_listNGC_188.ebf 0\nnbody1/cluster/cluster_listNGC_2323.ebf 0\nnbody1/cluster/cluster_listTrumpler_20.ebf 0\nnbody1/cluster/cluster_listIC_2714.ebf 0\nnbody1/cluster/cluster_listNGC_6475.ebf 0\nnbody1/cluster/cluster_listCollinder_110.ebf 0\nnbody1/cluster/cluster_listNGC_6259.ebf 0\nnbody1/cluster/cluster_listMelotte_22.ebf 0\nnbody1/cluster/cluster_listNGC_6067.ebf 0\nnbody1/cluster/cluster_listNGC_7654.ebf 0\nnbody1/cluster/cluster_listPismis_3.ebf 0\nnbody1/cluster/cluster_listStock_2.ebf 0\nnbody1/cluster/cluster_listNGC_3114.ebf 0\nnbody1/cluster/cluster_listNGC_2168.ebf 0\nnbody1/cluster/cluster_listNGC_6124.ebf 0\nnbody1/cluster/cluster_listNGC_2158.ebf 0\nnbody1/cluster/cluster_listNGC_6705.ebf 0\nnbody1/cluster/cluster_listNGC_2506.ebf 0\nnbody1/cluster/cluster_listNGC_6819.ebf 0\nnbody1/cluster/cluster_listNGC_2099.ebf 0\nnbody1/cluster/cluster_listNGC_2437.ebf 0\nnbody1/cluster/cluster_listCollinder_261.ebf 0\nnbody1/cluster/cluster_listNGC_3532.ebf 0\nnbody1/cluster/cluster_listNGC_2477.ebf 0\nnbody1/cluster/cluster_listTrumpler_5.ebf 0\nnbody1/cluster/cluster_listNGC_7789.ebf 0\nNo of Satellites =1118\nReading tabulated values from file- /home/rybizki/Programme/GalaxiaData/Model/vcirc.dat\nUsing geometry: All Sky\nReading Isochrones from dir- /home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\nzsol=0.0152\n/home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\n13275 75 177\nIsochrone Grid Size: (Age bins=177,Feh bins=75,Alpha bins=1)\nTime Isochrone Reading 1.49229 \n------------------------------\nnbody1/cluster/cluster_listLynga_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=301.274 0.493087\nTotal Stars=277 accepted=223 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1051.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=301.65 0.493087\nTotal Stars=213 accepted=170 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1397.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=302.415 0.493087\nTotal Stars=417 accepted=356 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_43.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=304.612 0.493087\nTotal Stars=400 accepted=306 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_72.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=304.896 0.493087\nTotal Stars=170 accepted=145 rejected=25\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_313_03.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=305.41 0.493087\nTotal Stars=146 accepted=115 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=306.314 0.493087\nTotal Stars=123 accepted=80 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_14A.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=307.855 0.493087\nTotal Stars=496 accepted=408 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=307.858 0.493087\nTotal Stars=252 accepted=196 rejected=56\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=308.13 0.493087\nTotal Stars=447 accepted=374 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1025.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=308.762 0.493087\nTotal Stars=209 accepted=177 rejected=32\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1580.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=308.79 0.493087\nTotal Stars=204 accepted=152 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=309.266 0.493087\nTotal Stars=254 accepted=190 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_42.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=310.447 0.493087\nTotal Stars=176 accepted=118 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_29.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=311.384 0.493087\nTotal Stars=106 accepted=76 rejected=30\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0401.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=312.085 0.493087\nTotal Stars=161 accepted=130 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0975.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=312.831 0.493087\nTotal Stars=166 accepted=125 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1419.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=313.87 0.493087\nTotal Stars=141 accepted=112 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_43.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=314.701 0.493087\nTotal Stars=179 accepted=127 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1172.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=314.863 0.493087\nTotal Stars=220 accepted=167 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIvanov_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=315.294 0.493087\nTotal Stars=398 accepted=322 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=315.321 0.493087\nTotal Stars=205 accepted=145 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_59.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=316.706 0.493087\nTotal Stars=248 accepted=179 rejected=69\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=316.836 0.493087\nTotal Stars=298 accepted=210 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_559_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=317.003 0.493087\nTotal Stars=227 accepted=167 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1460.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=317.042 0.493087\nTotal Stars=145 accepted=116 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=317.169 0.493087\nTotal Stars=240 accepted=165 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0284.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=317.279 0.493087\nTotal Stars=172 accepted=139 rejected=33\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0524.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=317.54 0.493087\nTotal Stars=188 accepted=137 rejected=51\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1335.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=318.33 0.493087\nTotal Stars=165 accepted=115 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=318.82 0.493087\nTotal Stars=361 accepted=266 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1624.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=318.994 0.493087\nTotal Stars=219 accepted=159 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_47.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=319.068 0.493087\nTotal Stars=170 accepted=111 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=319.206 0.493087\nTotal Stars=251 accepted=188 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_52.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=320.797 0.493087\nTotal Stars=175 accepted=140 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTurner_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=321.207 0.493087\nTotal Stars=293 accepted=235 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_54.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=321.383 0.493087\nTotal Stars=135 accepted=101 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=322.474 0.493087\nTotal Stars=191 accepted=143 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_50.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=323.052 0.493087\nTotal Stars=156 accepted=107 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_43.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=323.819 0.493087\nTotal Stars=227 accepted=182 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1171.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=324.311 0.493087\nTotal Stars=182 accepted=137 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listGraham_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=325.192 0.493087\nTotal Stars=164 accepted=125 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMamajek_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=325.793 0.493087\nTotal Stars=612 accepted=612 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSchuster_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=326.83 0.493087\nTotal Stars=444 accepted=353 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_27.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=326.884 0.493087\nTotal Stars=174 accepted=138 rejected=36\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1032.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=327.765 0.493087\nTotal Stars=250 accepted=184 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIvanov_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=329.594 0.493087\nTotal Stars=221 accepted=176 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1444.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=329.849 0.493087\nTotal Stars=485 accepted=416 rejected=69\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPlatais_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=330.609 0.493087\nTotal Stars=640 accepted=560 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_31.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=331.37 0.493087\nTotal Stars=181 accepted=143 rejected=38\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=332.572 0.493087\nTotal Stars=417 accepted=338 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_102.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=332.721 0.493087\nTotal Stars=184 accepted=135 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_393_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=335.147 0.493087\nTotal Stars=172 accepted=124 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0977.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=335.648 0.493087\nTotal Stars=251 accepted=210 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1352.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=335.852 0.493087\nTotal Stars=174 accepted=143 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_77.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=337.689 0.493087\nTotal Stars=156 accepted=137 rejected=19\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDB2001_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=337.85 0.493087\nTotal Stars=192 accepted=138 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPatchick_90.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=338.313 0.493087\nTotal Stars=147 accepted=107 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=339.062 0.493087\nTotal Stars=133 accepted=54 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=339.179 0.493087\nTotal Stars=250 accepted=195 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J0458+43.0.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=339.783 0.493087\nTotal Stars=251 accepted=181 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=339.972 0.493087\nTotal Stars=176 accepted=136 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=340.108 0.493087\nTotal Stars=369 accepted=263 rejected=106\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1170.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=340.93 0.493087\nTotal Stars=218 accepted=169 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=341.101 0.493087\nTotal Stars=196 accepted=134 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=341.242 0.493087\nTotal Stars=179 accepted=139 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPatchick_75.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=341.369 0.493087\nTotal Stars=268 accepted=191 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J0619+18.5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=341.761 0.493087\nTotal Stars=337 accepted=250 rejected=87\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=342.377 0.493087\nTotal Stars=190 accepted=156 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=342.394 0.493087\nTotal Stars=210 accepted=170 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=342.916 0.493087\nTotal Stars=154 accepted=115 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_82.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=343.335 0.493087\nTotal Stars=308 accepted=213 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBochum_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=343.476 0.493087\nTotal Stars=379 accepted=284 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_104.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=343.757 0.493087\nTotal Stars=311 accepted=250 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0158.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=344.141 0.493087\nTotal Stars=220 accepted=157 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0465.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=344.296 0.493087\nTotal Stars=163 accepted=129 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMarkarian_38.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=344.555 0.493087\nTotal Stars=414 accepted=343 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=344.911 0.493087\nTotal Stars=250 accepted=197 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_43.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=345.109 0.493087\nTotal Stars=266 accepted=195 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_211_09.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=346.611 0.493087\nTotal Stars=254 accepted=210 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1297.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=346.71 0.493087\nTotal Stars=541 accepted=461 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=347.061 0.493087\nTotal Stars=138 accepted=119 rejected=19\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=347.603 0.493087\nTotal Stars=311 accepted=244 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listToepler_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=348.297 0.493087\nTotal Stars=173 accepted=139 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTurner_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=348.447 0.493087\nTotal Stars=570 accepted=496 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_111.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=348.828 0.493087\nTotal Stars=196 accepted=162 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_57.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=349.309 0.493087\nTotal Stars=214 accepted=171 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_125.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=349.325 0.493087\nTotal Stars=249 accepted=172 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_226_06.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=349.758 0.493087\nTotal Stars=206 accepted=159 rejected=47\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1363.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=349.987 0.493087\nTotal Stars=209 accepted=167 rejected=42\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=351.327 0.493087\nTotal Stars=249 accepted=188 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=352.708 0.493087\nTotal Stars=256 accepted=206 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=353.197 0.493087\nTotal Stars=187 accepted=132 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_86.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=353.744 0.493087\nTotal Stars=457 accepted=379 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_60.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=354.527 0.493087\nTotal Stars=270 accepted=190 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDC_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=354.594 0.493087\nTotal Stars=174 accepted=148 rejected=26\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=354.604 0.493087\nTotal Stars=362 accepted=271 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0833.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=356.329 0.493087\nTotal Stars=184 accepted=144 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1117.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=356.713 0.493087\nTotal Stars=552 accepted=440 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=357.934 0.493087\nTotal Stars=300 accepted=224 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_54.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=358.182 0.493087\nTotal Stars=183 accepted=137 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBDSB91.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=358.432 0.493087\nTotal Stars=666 accepted=596 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBochum_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=358.974 0.493087\nTotal Stars=212 accepted=173 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_469.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=359.131 0.493087\nTotal Stars=212 accepted=177 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=359.966 0.493087\nTotal Stars=236 accepted=184 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=359.976 0.493087\nTotal Stars=122 accepted=100 rejected=22\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1724.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=360.121 0.493087\nTotal Stars=202 accepted=171 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPfleiderer_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=360.97 0.493087\nTotal Stars=159 accepted=127 rejected=32\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_149.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=361.646 0.493087\nTotal Stars=222 accepted=181 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listArp_Madore_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=363.006 0.493087\nTotal Stars=116 accepted=52 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBDSB30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=363.051 0.493087\nTotal Stars=626 accepted=621 rejected=5\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBDSB93.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=365.203 0.493087\nTotal Stars=617 accepted=535 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=365.498 0.493087\nTotal Stars=327 accepted=253 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0238.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=365.729 0.493087\nTotal Stars=490 accepted=408 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0683.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=366.105 0.493087\nTotal Stars=244 accepted=157 rejected=87\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0905.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=366.802 0.493087\nTotal Stars=327 accepted=266 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHavlen_Moffat_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=367.558 0.493087\nTotal Stars=209 accepted=147 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLDN_988e.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=367.597 0.493087\nTotal Stars=673 accepted=673 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=368.349 0.493087\nTotal Stars=252 accepted=204 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2580.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=369.291 0.493087\nTotal Stars=178 accepted=150 rejected=28\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7058.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=369.974 0.493087\nTotal Stars=646 accepted=573 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=370.035 0.493087\nTotal Stars=298 accepted=200 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=371.318 0.493087\nTotal Stars=232 accepted=186 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J2330+60.2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=371.451 0.493087\nTotal Stars=211 accepted=168 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0536.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=372.676 0.493087\nTotal Stars=184 accepted=149 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPatchick_94.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=373.066 0.493087\nTotal Stars=300 accepted=234 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_61.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=375.785 0.493087\nTotal Stars=215 accepted=157 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=376.582 0.493087\nTotal Stars=216 accepted=176 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=377.064 0.493087\nTotal Stars=186 accepted=145 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=377.284 0.493087\nTotal Stars=346 accepted=243 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAntalova_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=377.403 0.493087\nTotal Stars=721 accepted=596 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0826.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=377.833 0.493087\nTotal Stars=335 accepted=230 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0852.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=379.469 0.493087\nTotal Stars=229 accepted=180 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7024.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=379.902 0.493087\nTotal Stars=336 accepted=271 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_151.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=381.061 0.493087\nTotal Stars=251 accepted=191 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=381.982 0.493087\nTotal Stars=241 accepted=196 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_166_04.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=382.743 0.493087\nTotal Stars=380 accepted=277 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0430.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=383.518 0.493087\nTotal Stars=233 accepted=173 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0968.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=383.719 0.493087\nTotal Stars=273 accepted=200 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1125.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=383.925 0.493087\nTotal Stars=363 accepted=276 rejected=87\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1484.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=384.025 0.493087\nTotal Stars=236 accepted=181 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_225.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=384.036 0.493087\nTotal Stars=526 accepted=409 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6800.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=384.258 0.493087\nTotal Stars=377 accepted=285 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7129.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=385.448 0.493087\nTotal Stars=633 accepted=548 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_97.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=385.892 0.493087\nTotal Stars=584 accepted=487 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=386.24 0.493087\nTotal Stars=160 accepted=65 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_34.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=386.732 0.493087\nTotal Stars=152 accepted=90 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_368_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=387.556 0.493087\nTotal Stars=192 accepted=155 rejected=37\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0667.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=387.966 0.493087\nTotal Stars=380 accepted=302 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1183.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=388.134 0.493087\nTotal Stars=259 accepted=206 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFeibelman_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=388.363 0.493087\nTotal Stars=341 accepted=263 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_29.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=388.498 0.493087\nTotal Stars=269 accepted=225 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSher_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=388.545 0.493087\nTotal Stars=179 accepted=141 rejected=38\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=389.04 0.493087\nTotal Stars=256 accepted=182 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_83.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=389.08 0.493087\nTotal Stars=151 accepted=122 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=389.6 0.493087\nTotal Stars=317 accepted=236 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=389.792 0.493087\nTotal Stars=572 accepted=473 rejected=99\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=390.442 0.493087\nTotal Stars=486 accepted=376 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=390.552 0.493087\nTotal Stars=190 accepted=150 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=392.244 0.493087\nTotal Stars=253 accepted=190 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_115.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=392.353 0.493087\nTotal Stars=543 accepted=393 rejected=150\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_53.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=392.593 0.493087\nTotal Stars=251 accepted=187 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=393.484 0.493087\nTotal Stars=188 accepted=136 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_92.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=393.794 0.493087\nTotal Stars=179 accepted=135 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=394.167 0.493087\nTotal Stars=239 accepted=197 rejected=42\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_312_04.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=394.681 0.493087\nTotal Stars=261 accepted=218 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1399.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=395.143 0.493087\nTotal Stars=222 accepted=161 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1452.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=395.323 0.493087\nTotal Stars=254 accepted=189 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1509.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=395.624 0.493087\nTotal Stars=197 accepted=157 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2157.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=396.179 0.493087\nTotal Stars=293 accepted=234 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=396.365 0.493087\nTotal Stars=295 accepted=214 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_103.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=396.677 0.493087\nTotal Stars=340 accepted=275 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=397.036 0.493087\nTotal Stars=241 accepted=202 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMuzzio_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=399.234 0.493087\nTotal Stars=621 accepted=502 rejected=119\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_27.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=399.342 0.493087\nTotal Stars=312 accepted=245 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_34.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=399.424 0.493087\nTotal Stars=243 accepted=193 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listWaterloo_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=400.89 0.493087\nTotal Stars=234 accepted=194 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_91.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=402.209 0.493087\nTotal Stars=180 accepted=154 rejected=26\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0542.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=402.267 0.493087\nTotal Stars=207 accepted=178 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0883.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=402.64 0.493087\nTotal Stars=250 accepted=210 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_80.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=403.216 0.493087\nTotal Stars=192 accepted=162 rejected=30\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_151.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=403.482 0.493087\nTotal Stars=450 accepted=330 rejected=120\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=405.006 0.493087\nTotal Stars=226 accepted=185 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=405.212 0.493087\nTotal Stars=221 accepted=164 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_53.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=405.291 0.493087\nTotal Stars=411 accepted=316 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0296.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=405.32 0.493087\nTotal Stars=238 accepted=200 rejected=38\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2367.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=405.975 0.493087\nTotal Stars=365 accepted=256 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_76.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=406.121 0.493087\nTotal Stars=257 accepted=213 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSaurer_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=406.394 0.493087\nTotal Stars=198 accepted=123 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_33.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=407.357 0.493087\nTotal Stars=409 accepted=306 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=407.459 0.493087\nTotal Stars=179 accepted=139 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_92.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=407.936 0.493087\nTotal Stars=250 accepted=174 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=409.009 0.493087\nTotal Stars=423 accepted=306 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_245.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=409.131 0.493087\nTotal Stars=391 accepted=315 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0921.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=409.959 0.493087\nTotal Stars=255 accepted=200 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1063.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=410.003 0.493087\nTotal Stars=269 accepted=223 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1284.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=410.086 0.493087\nTotal Stars=266 accepted=206 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKharchenko_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=410.631 0.493087\nTotal Stars=298 accepted=246 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_84.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=410.86 0.493087\nTotal Stars=208 accepted=156 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLoden_46.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=411.008 0.493087\nTotal Stars=451 accepted=354 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_108.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=411.139 0.493087\nTotal Stars=359 accepted=283 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=411.729 0.493087\nTotal Stars=184 accepted=131 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_103.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=411.774 0.493087\nTotal Stars=271 accepted=193 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_61.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=411.819 0.493087\nTotal Stars=267 accepted=195 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_65.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=413.585 0.493087\nTotal Stars=445 accepted=343 rejected=102\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_101.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=413.604 0.493087\nTotal Stars=289 accepted=218 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=414.094 0.493087\nTotal Stars=300 accepted=227 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0553.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=414.825 0.493087\nTotal Stars=272 accepted=215 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1260.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=416.145 0.493087\nTotal Stars=238 accepted=188 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=416.804 0.493087\nTotal Stars=252 accepted=198 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=417.087 0.493087\nTotal Stars=241 accepted=173 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_69.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=417.352 0.493087\nTotal Stars=233 accepted=199 rejected=34\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMarkarian_50.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=417.628 0.493087\nTotal Stars=359 accepted=261 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5606.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=418.179 0.493087\nTotal Stars=328 accepted=247 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6178.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=418.46 0.493087\nTotal Stars=638 accepted=491 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPatchick_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=419.124 0.493087\nTotal Stars=340 accepted=256 rejected=84\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_148.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=419.207 0.493087\nTotal Stars=256 accepted=220 rejected=36\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=420.77 0.493087\nTotal Stars=238 accepted=145 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_44.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=420.928 0.493087\nTotal Stars=172 accepted=126 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_67.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=421.22 0.493087\nTotal Stars=313 accepted=223 rejected=90\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1150.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=421.489 0.493087\nTotal Stars=243 accepted=188 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1212.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=421.554 0.493087\nTotal Stars=232 accepted=175 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2588.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=422.025 0.493087\nTotal Stars=234 accepted=172 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=422.131 0.493087\nTotal Stars=433 accepted=344 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTurner_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=422.919 0.493087\nTotal Stars=370 accepted=275 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0384.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=422.987 0.493087\nTotal Stars=503 accepted=412 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0448.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=422.99 0.493087\nTotal Stars=274 accepted=206 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_81.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=423.326 0.493087\nTotal Stars=209 accepted=168 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7160.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=423.593 0.493087\nTotal Stars=702 accepted=584 rejected=118\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=423.923 0.493087\nTotal Stars=448 accepted=362 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_97.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=424.285 0.493087\nTotal Stars=205 accepted=162 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=424.776 0.493087\nTotal Stars=495 accepted=387 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=426.082 0.493087\nTotal Stars=818 accepted=818 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=426.136 0.493087\nTotal Stars=208 accepted=167 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_44.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=426.36 0.493087\nTotal Stars=200 accepted=169 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_50.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=428.978 0.493087\nTotal Stars=317 accepted=238 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_54.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=429.963 0.493087\nTotal Stars=342 accepted=256 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_75.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=430.135 0.493087\nTotal Stars=203 accepted=121 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_42.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=431.897 0.493087\nTotal Stars=317 accepted=226 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0948.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=432.909 0.493087\nTotal Stars=217 accepted=175 rejected=42\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=433.239 0.493087\nTotal Stars=255 accepted=207 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6613.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=435.645 0.493087\nTotal Stars=513 accepted=361 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_71.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=435.786 0.493087\nTotal Stars=281 accepted=214 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_78.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=436.511 0.493087\nTotal Stars=150 accepted=119 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0172.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=437.07 0.493087\nTotal Stars=239 accepted=190 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0357.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=437.303 0.493087\nTotal Stars=242 accepted=190 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1530.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=437.419 0.493087\nTotal Stars=234 accepted=179 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=437.748 0.493087\nTotal Stars=327 accepted=235 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=438.055 0.493087\nTotal Stars=257 accepted=221 rejected=36\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_53.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=438.382 0.493087\nTotal Stars=205 accepted=176 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1333.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=438.609 0.493087\nTotal Stars=854 accepted=854 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=438.612 0.493087\nTotal Stars=517 accepted=416 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_35.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=438.821 0.493087\nTotal Stars=221 accepted=186 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_108.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=439.609 0.493087\nTotal Stars=228 accepted=182 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=440.429 0.493087\nTotal Stars=271 accepted=227 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_61.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=441.784 0.493087\nTotal Stars=274 accepted=231 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_87.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=442.035 0.493087\nTotal Stars=523 accepted=398 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAuner_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=442.059 0.493087\nTotal Stars=194 accepted=124 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_67.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=443.291 0.493087\nTotal Stars=190 accepted=155 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_73.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=445 0.493087\nTotal Stars=182 accepted=122 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_419.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=447.832 0.493087\nTotal Stars=660 accepted=553 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=448.148 0.493087\nTotal Stars=400 accepted=315 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0811.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=449.388 0.493087\nTotal Stars=291 accepted=230 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0935.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=451.062 0.493087\nTotal Stars=289 accepted=222 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2269.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=451.156 0.493087\nTotal Stars=314 accepted=263 rejected=51\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5764.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=451.18 0.493087\nTotal Stars=310 accepted=242 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7226.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=451.575 0.493087\nTotal Stars=228 accepted=173 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_102.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=453.955 0.493087\nTotal Stars=242 accepted=180 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=454.021 0.493087\nTotal Stars=405 accepted=324 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=454.578 0.493087\nTotal Stars=298 accepted=222 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_101.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=454.939 0.493087\nTotal Stars=229 accepted=184 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_104.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=455.886 0.493087\nTotal Stars=230 accepted=182 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=456.221 0.493087\nTotal Stars=206 accepted=149 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_76.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=456.565 0.493087\nTotal Stars=253 accepted=200 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_94.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=457.497 0.493087\nTotal Stars=246 accepted=205 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_100.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=458.709 0.493087\nTotal Stars=319 accepted=249 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=458.71 0.493087\nTotal Stars=537 accepted=439 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0537.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=459.004 0.493087\nTotal Stars=248 accepted=180 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0551.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=459.596 0.493087\nTotal Stars=867 accepted=770 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0923.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=460.493 0.493087\nTotal Stars=360 accepted=280 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1435.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=461.73 0.493087\nTotal Stars=629 accepted=515 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=462.938 0.493087\nTotal Stars=317 accepted=245 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_106.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=462.972 0.493087\nTotal Stars=196 accepted=159 rejected=37\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_83.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=463.445 0.493087\nTotal Stars=563 accepted=358 rejected=205\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=464.138 0.493087\nTotal Stars=231 accepted=196 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_271.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=464.245 0.493087\nTotal Stars=364 accepted=242 rejected=122\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_63.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=464.668 0.493087\nTotal Stars=227 accepted=167 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNegueruela_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=464.932 0.493087\nTotal Stars=370 accepted=292 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRiddle_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=465.472 0.493087\nTotal Stars=345 accepted=269 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_29.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=465.599 0.493087\nTotal Stars=567 accepted=433 rejected=134\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=465.77 0.493087\nTotal Stars=387 accepted=281 rejected=106\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDias_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=465.86 0.493087\nTotal Stars=260 accepted=181 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=466.162 0.493087\nTotal Stars=234 accepted=194 rejected=40\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5281.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=466.647 0.493087\nTotal Stars=428 accepted=331 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=468.867 0.493087\nTotal Stars=421 accepted=321 rejected=100\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_105.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=469.587 0.493087\nTotal Stars=307 accepted=236 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_130.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=469.808 0.493087\nTotal Stars=280 accepted=243 rejected=37\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_123.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=470.921 0.493087\nTotal Stars=895 accepted=833 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=470.99 0.493087\nTotal Stars=641 accepted=512 rejected=129\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_150.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=471.136 0.493087\nTotal Stars=199 accepted=179 rejected=20\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=471.79 0.493087\nTotal Stars=265 accepted=224 rejected=41\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_092_05.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=475.379 0.493087\nTotal Stars=226 accepted=160 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=475.85 0.493087\nTotal Stars=337 accepted=276 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=475.944 0.493087\nTotal Stars=741 accepted=631 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_75.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=476.535 0.493087\nTotal Stars=239 accepted=181 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=476.76 0.493087\nTotal Stars=492 accepted=382 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_63.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=477.405 0.493087\nTotal Stars=231 accepted=185 rejected=46\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0195.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=477.584 0.493087\nTotal Stars=221 accepted=159 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMayer_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=478.588 0.493087\nTotal Stars=317 accepted=231 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1579.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=478.765 0.493087\nTotal Stars=926 accepted=926 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_135.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=480.193 0.493087\nTotal Stars=504 accepted=319 rejected=185\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=480.978 0.493087\nTotal Stars=260 accepted=211 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_27.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=481.174 0.493087\nTotal Stars=253 accepted=200 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0985.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=482.089 0.493087\nTotal Stars=345 accepted=287 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1441.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=482.247 0.493087\nTotal Stars=241 accepted=205 rejected=36\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1595.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=482.717 0.493087\nTotal Stars=297 accepted=238 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHarvard_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=482.992 0.493087\nTotal Stars=506 accepted=324 rejected=182\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1220.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=483.081 0.493087\nTotal Stars=307 accepted=250 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_189.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=483.097 0.493087\nTotal Stars=443 accepted=348 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6469.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=483.787 0.493087\nTotal Stars=377 accepted=287 rejected=90\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7281.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=484.226 0.493087\nTotal Stars=345 accepted=268 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J0614+12.9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=484.656 0.493087\nTotal Stars=306 accepted=245 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=485.207 0.493087\nTotal Stars=307 accepted=227 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0398.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=485.904 0.493087\nTotal Stars=673 accepted=504 rejected=169\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=486.149 0.493087\nTotal Stars=355 accepted=275 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1496.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=486.751 0.493087\nTotal Stars=350 accepted=274 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_144.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=486.976 0.493087\nTotal Stars=332 accepted=274 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listWaterloo_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=488.235 0.493087\nTotal Stars=258 accepted=209 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_107.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=488.386 0.493087\nTotal Stars=593 accepted=492 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=488.629 0.493087\nTotal Stars=570 accepted=440 rejected=130\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_73.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=489.8 0.493087\nTotal Stars=336 accepted=252 rejected=84\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_092_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=490.472 0.493087\nTotal Stars=185 accepted=127 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1207.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=490.503 0.493087\nTotal Stars=315 accepted=242 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1380.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=490.642 0.493087\nTotal Stars=275 accepted=213 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBarkhatova_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=491.317 0.493087\nTotal Stars=358 accepted=264 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0932.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=491.595 0.493087\nTotal Stars=344 accepted=258 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1360.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=492.128 0.493087\nTotal Stars=288 accepted=226 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2225.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=493.09 0.493087\nTotal Stars=311 accepted=221 rejected=90\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2302.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=493.143 0.493087\nTotal Stars=499 accepted=383 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6249.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=493.528 0.493087\nTotal Stars=406 accepted=314 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_33.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=494.444 0.493087\nTotal Stars=335 accepted=259 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_67.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=494.711 0.493087\nTotal Stars=369 accepted=280 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_14a.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=494.744 0.493087\nTotal Stars=309 accepted=249 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_47.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=497.531 0.493087\nTotal Stars=301 accepted=228 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_96.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=498.208 0.493087\nTotal Stars=369 accepted=263 rejected=106\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_39.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=499.109 0.493087\nTotal Stars=273 accepted=234 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_371_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=500.752 0.493087\nTotal Stars=302 accepted=228 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=503.512 0.493087\nTotal Stars=213 accepted=114 rejected=99\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMon_OB1_D.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=503.529 0.493087\nTotal Stars=587 accepted=459 rejected=128\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDias_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=503.611 0.493087\nTotal Stars=369 accepted=298 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0167.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=503.937 0.493087\nTotal Stars=342 accepted=290 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0385.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=504.639 0.493087\nTotal Stars=365 accepted=279 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_743.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=504.656 0.493087\nTotal Stars=601 accepted=432 rejected=169\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_107.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=505.063 0.493087\nTotal Stars=233 accepted=204 rejected=29\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=506.086 0.493087\nTotal Stars=320 accepted=244 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=507.367 0.493087\nTotal Stars=387 accepted=298 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=507.696 0.493087\nTotal Stars=613 accepted=508 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=508.733 0.493087\nTotal Stars=363 accepted=274 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_134_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=508.75 0.493087\nTotal Stars=330 accepted=266 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0728.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=510.31 0.493087\nTotal Stars=400 accepted=307 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=513.728 0.493087\nTotal Stars=229 accepted=190 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=514.682 0.493087\nTotal Stars=352 accepted=286 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_Moreno_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=515.105 0.493087\nTotal Stars=743 accepted=592 rejected=151\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=515.653 0.493087\nTotal Stars=282 accepted=219 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_28.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=516.482 0.493087\nTotal Stars=272 accepted=230 rejected=42\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_95.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=518.489 0.493087\nTotal Stars=277 accepted=229 rejected=48\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=518.95 0.493087\nTotal Stars=334 accepted=276 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_311_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=519.107 0.493087\nTotal Stars=220 accepted=155 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_312_03.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=519.569 0.493087\nTotal Stars=250 accepted=206 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2343.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=519.837 0.493087\nTotal Stars=546 accepted=414 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_34.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=520.388 0.493087\nTotal Stars=309 accepted=228 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_132.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=520.879 0.493087\nTotal Stars=319 accepted=258 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=522.666 0.493087\nTotal Stars=265 accepted=149 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0866.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=524.431 0.493087\nTotal Stars=539 accepted=370 rejected=169\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=525.829 0.493087\nTotal Stars=301 accepted=252 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4439.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=526.04 0.493087\nTotal Stars=428 accepted=311 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_47.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=526.096 0.493087\nTotal Stars=292 accepted=242 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_94.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=526.156 0.493087\nTotal Stars=274 accepted=199 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_28.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=526.196 0.493087\nTotal Stars=273 accepted=217 rejected=56\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_52.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=527.566 0.493087\nTotal Stars=254 accepted=201 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0735.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=527.777 0.493087\nTotal Stars=355 accepted=275 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_146.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=528.721 0.493087\nTotal Stars=328 accepted=263 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=528.738 0.493087\nTotal Stars=230 accepted=214 rejected=16\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J1942+38.6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=529.018 0.493087\nTotal Stars=338 accepted=240 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listArchinal_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=529.402 0.493087\nTotal Stars=366 accepted=289 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=530.056 0.493087\nTotal Stars=386 accepted=306 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_77.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=530.489 0.493087\nTotal Stars=276 accepted=221 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDanks_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=531.315 0.493087\nTotal Stars=657 accepted=517 rejected=140\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1211.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=531.346 0.493087\nTotal Stars=324 accepted=230 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_79.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=531.664 0.493087\nTotal Stars=308 accepted=250 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7063.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=533.543 0.493087\nTotal Stars=808 accepted=661 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_100.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=533.781 0.493087\nTotal Stars=268 accepted=211 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_126.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=533.794 0.493087\nTotal Stars=354 accepted=269 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_35.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=533.937 0.493087\nTotal Stars=327 accepted=277 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_73.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=534.203 0.493087\nTotal Stars=697 accepted=562 rejected=135\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_118.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=534.494 0.493087\nTotal Stars=633 accepted=422 rejected=211\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_72.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=535.035 0.493087\nTotal Stars=350 accepted=290 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBiurakan_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=535.542 0.493087\nTotal Stars=588 accepted=467 rejected=121\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0941.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=536.421 0.493087\nTotal Stars=305 accepted=237 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1085.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=537.104 0.493087\nTotal Stars=403 accepted=323 rejected=80\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=537.206 0.493087\nTotal Stars=343 accepted=277 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=537.44 0.493087\nTotal Stars=307 accepted=241 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLoden_1194.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=537.913 0.493087\nTotal Stars=644 accepted=512 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7067.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=538.123 0.493087\nTotal Stars=360 accepted=305 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_42.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=539.609 0.493087\nTotal Stars=432 accepted=331 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_90.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=539.753 0.493087\nTotal Stars=732 accepted=607 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_185.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=540.012 0.493087\nTotal Stars=421 accepted=347 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=540.412 0.493087\nTotal Stars=509 accepted=411 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_91.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=541.046 0.493087\nTotal Stars=308 accepted=237 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=541.631 0.493087\nTotal Stars=457 accepted=327 rejected=130\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_110.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=542.023 0.493087\nTotal Stars=363 accepted=274 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_27.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=542.6 0.493087\nTotal Stars=269 accepted=188 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_338.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=542.649 0.493087\nTotal Stars=762 accepted=652 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDBSB_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=542.977 0.493087\nTotal Stars=403 accepted=290 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0974.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=543.727 0.493087\nTotal Stars=296 accepted=232 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2948.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=543.942 0.493087\nTotal Stars=574 accepted=449 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6322.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=544.107 0.493087\nTotal Stars=683 accepted=562 rejected=121\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6846.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=544.793 0.493087\nTotal Stars=276 accepted=201 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_41.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=544.829 0.493087\nTotal Stars=273 accepted=214 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=545.546 0.493087\nTotal Stars=461 accepted=379 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=545.62 0.493087\nTotal Stars=640 accepted=546 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_99.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=545.79 0.493087\nTotal Stars=924 accepted=839 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_72.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=546.088 0.493087\nTotal Stars=351 accepted=281 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBochum_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=546.126 0.493087\nTotal Stars=640 accepted=504 rejected=136\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDanks_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=546.224 0.493087\nTotal Stars=741 accepted=617 rejected=124\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=546.978 0.493087\nTotal Stars=278 accepted=243 rejected=35\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=548.009 0.493087\nTotal Stars=336 accepted=265 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=548.478 0.493087\nTotal Stars=339 accepted=268 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_36.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=550.396 0.493087\nTotal Stars=321 accepted=260 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_94.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=551.154 0.493087\nTotal Stars=464 accepted=371 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_128.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=554.481 0.493087\nTotal Stars=740 accepted=636 rejected=104\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_269.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=554.494 0.493087\nTotal Stars=435 accepted=344 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_130_06.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=554.837 0.493087\nTotal Stars=914 accepted=750 rejected=164\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6396.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=555.505 0.493087\nTotal Stars=392 accepted=269 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=555.633 0.493087\nTotal Stars=890 accepted=742 rejected=148\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBochum_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=556.116 0.493087\nTotal Stars=660 accepted=517 rejected=143\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=559.697 0.493087\nTotal Stars=286 accepted=209 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=560.001 0.493087\nTotal Stars=422 accepted=273 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5269.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=560.586 0.493087\nTotal Stars=378 accepted=317 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=560.839 0.493087\nTotal Stars=405 accepted=333 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=561.004 0.493087\nTotal Stars=895 accepted=743 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0534.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=561.518 0.493087\nTotal Stars=356 accepted=293 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=561.672 0.493087\nTotal Stars=350 accepted=284 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2183.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=562.477 0.493087\nTotal Stars=924 accepted=748 rejected=176\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=564.04 0.493087\nTotal Stars=374 accepted=300 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_101.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=564.584 0.493087\nTotal Stars=849 accepted=754 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1901.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=564.958 0.493087\nTotal Stars=824 accepted=723 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_84.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=565.916 0.493087\nTotal Stars=392 accepted=303 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_84.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=566.097 0.493087\nTotal Stars=288 accepted=244 rejected=44\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_Saloran_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=566.195 0.493087\nTotal Stars=278 accepted=177 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLoden_372.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=566.754 0.493087\nTotal Stars=362 accepted=306 rejected=56\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=567.867 0.493087\nTotal Stars=397 accepted=315 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_637.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=568.55 0.493087\nTotal Stars=611 accepted=488 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6520.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=569.082 0.493087\nTotal Stars=449 accepted=351 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7296.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=569.313 0.493087\nTotal Stars=362 accepted=294 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=569.641 0.493087\nTotal Stars=298 accepted=244 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_48.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=570.942 0.493087\nTotal Stars=267 accepted=218 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_81.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=570.993 0.493087\nTotal Stars=331 accepted=269 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_71.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=571.77 0.493087\nTotal Stars=319 accepted=227 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=572.087 0.493087\nTotal Stars=289 accepted=234 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6031.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=572.1 0.493087\nTotal Stars=446 accepted=330 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6561.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=573.326 0.493087\nTotal Stars=858 accepted=722 rejected=136\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_127.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=573.543 0.493087\nTotal Stars=397 accepted=312 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_144.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=575.164 0.493087\nTotal Stars=364 accepted=273 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBDSB96.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=575.483 0.493087\nTotal Stars=901 accepted=720 rejected=181\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=575.625 0.493087\nTotal Stars=358 accepted=295 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_4996.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=576.449 0.493087\nTotal Stars=596 accepted=482 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=576.828 0.493087\nTotal Stars=427 accepted=322 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2374.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=577.185 0.493087\nTotal Stars=510 accepted=417 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_113.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=578.647 0.493087\nTotal Stars=245 accepted=186 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_80.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=579.052 0.493087\nTotal Stars=1123 accepted=1091 rejected=32\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_60.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=579.52 0.493087\nTotal Stars=366 accepted=301 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_55.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=579.788 0.493087\nTotal Stars=300 accepted=234 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_136.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=580.708 0.493087\nTotal Stars=283 accepted=234 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2129.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=581.422 0.493087\nTotal Stars=527 accepted=402 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5749.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=581.552 0.493087\nTotal Stars=601 accepted=469 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=582.178 0.493087\nTotal Stars=364 accepted=269 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0166.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=582.223 0.493087\nTotal Stars=322 accepted=248 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0306.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=583.105 0.493087\nTotal Stars=413 accepted=318 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1591.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=583.345 0.493087\nTotal Stars=300 accepted=238 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=583.402 0.493087\nTotal Stars=680 accepted=536 rejected=144\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_145.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=583.698 0.493087\nTotal Stars=347 accepted=253 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAveni_Hunter_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=584.372 0.493087\nTotal Stars=953 accepted=820 rejected=133\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_144.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=585.182 0.493087\nTotal Stars=240 accepted=187 rejected=53\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=585.185 0.493087\nTotal Stars=451 accepted=348 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_44.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=586.569 0.493087\nTotal Stars=428 accepted=316 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1342.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=587.954 0.493087\nTotal Stars=316 accepted=266 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_36.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=589.218 0.493087\nTotal Stars=446 accepted=357 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_744.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=590.295 0.493087\nTotal Stars=512 accepted=411 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_161.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=590.445 0.493087\nTotal Stars=659 accepted=532 rejected=127\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_37.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=592.501 0.493087\nTotal Stars=297 accepted=176 rejected=121\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=592.882 0.493087\nTotal Stars=859 accepted=723 rejected=136\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_11b.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=594.254 0.493087\nTotal Stars=437 accepted=339 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=594.493 0.493087\nTotal Stars=296 accepted=237 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKronberger_52.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=595.284 0.493087\nTotal Stars=364 accepted=261 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=595.478 0.493087\nTotal Stars=318 accepted=256 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1252.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=596.943 0.493087\nTotal Stars=312 accepted=242 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1521.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=597.414 0.493087\nTotal Stars=228 accepted=195 rejected=33\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=598.524 0.493087\nTotal Stars=336 accepted=278 rejected=58\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=599.309 0.493087\nTotal Stars=428 accepted=339 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6250.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=599.611 0.493087\nTotal Stars=884 accepted=658 rejected=226\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_80.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=600.821 0.493087\nTotal Stars=366 accepted=297 rejected=69\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6716.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=601.069 0.493087\nTotal Stars=991 accepted=820 rejected=171\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_109.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=601.724 0.493087\nTotal Stars=254 accepted=194 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0165.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=602.825 0.493087\nTotal Stars=589 accepted=444 rejected=145\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1180.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=603.234 0.493087\nTotal Stars=355 accepted=267 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1716.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=603.373 0.493087\nTotal Stars=364 accepted=252 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=603.882 0.493087\nTotal Stars=625 accepted=490 rejected=135\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_31.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=604.712 0.493087\nTotal Stars=356 accepted=280 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0282.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=605.286 0.493087\nTotal Stars=345 accepted=240 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=605.348 0.493087\nTotal Stars=476 accepted=378 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBica_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=605.543 0.493087\nTotal Stars=507 accepted=380 rejected=127\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_167.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=606.793 0.493087\nTotal Stars=675 accepted=539 rejected=136\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=606.812 0.493087\nTotal Stars=814 accepted=636 rejected=178\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_51.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=607.75 0.493087\nTotal Stars=335 accepted=283 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_49.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=607.867 0.493087\nTotal Stars=289 accepted=247 rejected=42\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_90.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=607.996 0.493087\nTotal Stars=330 accepted=287 rejected=43\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4463.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=608.027 0.493087\nTotal Stars=405 accepted=327 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPlatais_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=608.103 0.493087\nTotal Stars=1150 accepted=1003 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_86.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=608.271 0.493087\nTotal Stars=314 accepted=240 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=609.062 0.493087\nTotal Stars=317 accepted=243 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_49.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=609.092 0.493087\nTotal Stars=398 accepted=310 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=609.93 0.493087\nTotal Stars=411 accepted=329 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDias_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=610.562 0.493087\nTotal Stars=600 accepted=423 rejected=177\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=610.644 0.493087\nTotal Stars=575 accepted=463 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2401.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=611.778 0.493087\nTotal Stars=418 accepted=294 rejected=124\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_126.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=611.996 0.493087\nTotal Stars=297 accepted=229 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_54.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=612.503 0.493087\nTotal Stars=356 accepted=258 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=612.825 0.493087\nTotal Stars=364 accepted=276 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=613.064 0.493087\nTotal Stars=440 accepted=332 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0716.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=613.47 0.493087\nTotal Stars=340 accepted=261 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1348.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=613.933 0.493087\nTotal Stars=409 accepted=301 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2358.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=614.993 0.493087\nTotal Stars=732 accepted=513 rejected=219\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2567.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=615.849 0.493087\nTotal Stars=475 accepted=378 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5593.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=616.033 0.493087\nTotal Stars=645 accepted=466 rejected=179\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=616.927 0.493087\nTotal Stars=934 accepted=768 rejected=166\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_71.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=617.372 0.493087\nTotal Stars=600 accepted=453 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=617.764 0.493087\nTotal Stars=265 accepted=197 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_97.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=617.938 0.493087\nTotal Stars=388 accepted=283 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2972.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=618.004 0.493087\nTotal Stars=441 accepted=360 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7261.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=618.895 0.493087\nTotal Stars=340 accepted=267 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=618.985 0.493087\nTotal Stars=366 accepted=299 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=619.806 0.493087\nTotal Stars=1167 accepted=1167 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_99.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=619.978 0.493087\nTotal Stars=324 accepted=210 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=621.078 0.493087\nTotal Stars=289 accepted=200 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=622.105 0.493087\nTotal Stars=381 accepted=312 rejected=69\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_174.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=622.822 0.493087\nTotal Stars=395 accepted=300 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=624.448 0.493087\nTotal Stars=493 accepted=384 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_54.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=624.569 0.493087\nTotal Stars=326 accepted=248 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0198.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=625.122 0.493087\nTotal Stars=806 accepted=654 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2925.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=626.069 0.493087\nTotal Stars=857 accepted=730 rejected=127\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAndrews_Lindsay_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=626.537 0.493087\nTotal Stars=714 accepted=590 rejected=124\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1407.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=626.858 0.493087\nTotal Stars=352 accepted=246 rejected=106\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1663.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=627.136 0.493087\nTotal Stars=348 accepted=284 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_170.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=628.476 0.493087\nTotal Stars=769 accepted=611 rejected=158\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_172.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=628.525 0.493087\nTotal Stars=316 accepted=255 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=629.298 0.493087\nTotal Stars=294 accepted=249 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_74.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=629.519 0.493087\nTotal Stars=309 accepted=255 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2659.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=630.013 0.493087\nTotal Stars=563 accepted=415 rejected=148\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2866.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=630.538 0.493087\nTotal Stars=414 accepted=339 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_93.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=633.283 0.493087\nTotal Stars=447 accepted=338 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStephenson_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=634.492 0.493087\nTotal Stars=1227 accepted=1227 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_132.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=636.018 0.493087\nTotal Stars=1109 accepted=897 rejected=212\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1590.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=636.218 0.493087\nTotal Stars=639 accepted=480 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=636.474 0.493087\nTotal Stars=322 accepted=251 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3680.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=636.639 0.493087\nTotal Stars=634 accepted=474 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_117.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=637.508 0.493087\nTotal Stars=325 accepted=249 rejected=76\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J0507+30.8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=637.918 0.493087\nTotal Stars=343 accepted=230 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1402.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=638.647 0.493087\nTotal Stars=291 accepted=228 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listL_1641S.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=641.3 0.493087\nTotal Stars=1193 accepted=1193 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1883.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=642.631 0.493087\nTotal Stars=330 accepted=257 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_51.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=642.666 0.493087\nTotal Stars=326 accepted=287 rejected=39\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_205.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=643.746 0.493087\nTotal Stars=712 accepted=562 rejected=150\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=643.887 0.493087\nTotal Stars=596 accepted=465 rejected=131\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_589_26.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=645.017 0.493087\nTotal Stars=678 accepted=509 rejected=169\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2184.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=646.479 0.493087\nTotal Stars=864 accepted=717 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2311.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=646.491 0.493087\nTotal Stars=448 accepted=365 rejected=83\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3033.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=646.632 0.493087\nTotal Stars=463 accepted=369 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_78.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=647.15 0.493087\nTotal Stars=736 accepted=596 rejected=140\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_35.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=648.464 0.493087\nTotal Stars=343 accepted=264 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1083.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=648.653 0.493087\nTotal Stars=365 accepted=276 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_83.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=649.422 0.493087\nTotal Stars=552 accepted=407 rejected=145\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBasel_11a.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=649.824 0.493087\nTotal Stars=900 accepted=694 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1750.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=650.603 0.493087\nTotal Stars=305 accepted=249 rejected=56\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2533.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=650.642 0.493087\nTotal Stars=384 accepted=302 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6866.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=651.026 0.493087\nTotal Stars=591 accepted=449 rejected=142\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=651.242 0.493087\nTotal Stars=333 accepted=259 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=652.036 0.493087\nTotal Stars=315 accepted=255 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_292.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=652.11 0.493087\nTotal Stars=483 accepted=368 rejected=115\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0953.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=653.472 0.493087\nTotal Stars=406 accepted=334 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6357.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=653.586 0.493087\nTotal Stars=902 accepted=764 rejected=138\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=654.18 0.493087\nTotal Stars=639 accepted=465 rejected=174\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_124.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=654.552 0.493087\nTotal Stars=903 accepted=694 rejected=209\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_45.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=654.563 0.493087\nTotal Stars=317 accepted=258 rejected=59\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=655.724 0.493087\nTotal Stars=543 accepted=391 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3105.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=656.794 0.493087\nTotal Stars=220 accepted=189 rejected=31\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_143.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=656.808 0.493087\nTotal Stars=348 accepted=294 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=658.068 0.493087\nTotal Stars=369 accepted=281 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_63.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=658.119 0.493087\nTotal Stars=330 accepted=262 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=658.898 0.493087\nTotal Stars=564 accepted=412 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0275.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=659.09 0.493087\nTotal Stars=355 accepted=277 rejected=78\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_5146.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=659.621 0.493087\nTotal Stars=1227 accepted=1227 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_37.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=660.4 0.493087\nTotal Stars=325 accepted=258 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_60.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=660.83 0.493087\nTotal Stars=351 accepted=285 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=660.993 0.493087\nTotal Stars=528 accepted=409 rejected=119\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5138.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=662.459 0.493087\nTotal Stars=528 accepted=379 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6631.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=663.032 0.493087\nTotal Stars=365 accepted=290 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_82.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=664.267 0.493087\nTotal Stars=494 accepted=387 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=664.719 0.493087\nTotal Stars=996 accepted=847 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=665.947 0.493087\nTotal Stars=885 accepted=669 rejected=216\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_211.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=666.12 0.493087\nTotal Stars=430 accepted=351 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1723.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=667.529 0.493087\nTotal Stars=498 accepted=417 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2186.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=667.593 0.493087\nTotal Stars=472 accepted=405 rejected=67\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_433.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=668.714 0.493087\nTotal Stars=522 accepted=411 rejected=111\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_98.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=669.913 0.493087\nTotal Stars=944 accepted=794 rejected=150\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=669.918 0.493087\nTotal Stars=752 accepted=587 rejected=165\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=670.214 0.493087\nTotal Stars=384 accepted=275 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=670.236 0.493087\nTotal Stars=679 accepted=508 rejected=171\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_119.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=670.641 0.493087\nTotal Stars=456 accepted=352 rejected=104\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_200.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=673.009 0.493087\nTotal Stars=526 accepted=372 rejected=154\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_69.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=673.179 0.493087\nTotal Stars=381 accepted=289 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_40.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=673.298 0.493087\nTotal Stars=331 accepted=243 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_368_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=673.582 0.493087\nTotal Stars=388 accepted=307 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5288.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=675.51 0.493087\nTotal Stars=402 accepted=309 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7062.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=675.795 0.493087\nTotal Stars=394 accepted=294 rejected=100\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=675.946 0.493087\nTotal Stars=389 accepted=337 rejected=52\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=676.481 0.493087\nTotal Stars=911 accepted=758 rejected=153\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=677.025 0.493087\nTotal Stars=363 accepted=193 rejected=170\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_106.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=678.217 0.493087\nTotal Stars=1022 accepted=862 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_112.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=679.944 0.493087\nTotal Stars=951 accepted=763 rejected=188\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_62.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=680.244 0.493087\nTotal Stars=570 accepted=428 rejected=142\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=681.813 0.493087\nTotal Stars=388 accepted=319 rejected=69\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=682.468 0.493087\nTotal Stars=925 accepted=668 rejected=257\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=682.843 0.493087\nTotal Stars=417 accepted=345 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=684.801 0.493087\nTotal Stars=422 accepted=327 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_96.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=686.542 0.493087\nTotal Stars=411 accepted=300 rejected=111\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_130_08.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=687.576 0.493087\nTotal Stars=595 accepted=458 rejected=137\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_74.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=688.231 0.493087\nTotal Stars=454 accepted=353 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2259.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=688.257 0.493087\nTotal Stars=424 accepted=329 rejected=95\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_134.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=688.364 0.493087\nTotal Stars=407 accepted=334 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=688.704 0.493087\nTotal Stars=528 accepted=414 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=689.253 0.493087\nTotal Stars=1036 accepted=852 rejected=184\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=689.521 0.493087\nTotal Stars=449 accepted=338 rejected=111\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3255.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=689.871 0.493087\nTotal Stars=401 accepted=310 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7039.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=690.226 0.493087\nTotal Stars=950 accepted=695 rejected=255\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_80.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=693.5 0.493087\nTotal Stars=410 accepted=329 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_55.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=694.082 0.493087\nTotal Stars=373 accepted=303 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3572.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=694.582 0.493087\nTotal Stars=683 accepted=523 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7380.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=695.334 0.493087\nTotal Stars=535 accepted=380 rejected=155\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=695.423 0.493087\nTotal Stars=383 accepted=300 rejected=83\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=695.699 0.493087\nTotal Stars=434 accepted=329 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2304.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=696.009 0.493087\nTotal Stars=397 accepted=336 rejected=61\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3228.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=696.158 0.493087\nTotal Stars=1069 accepted=916 rejected=153\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3330.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=696.888 0.493087\nTotal Stars=546 accepted=385 rejected=161\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_138.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=698.278 0.493087\nTotal Stars=431 accepted=339 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_127.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=698.381 0.493087\nTotal Stars=1343 accepted=1227 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_56.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=699.324 0.493087\nTotal Stars=1063 accepted=896 rejected=167\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1857.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=700.244 0.493087\nTotal Stars=451 accepted=349 rejected=102\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_115.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=700.453 0.493087\nTotal Stars=455 accepted=327 rejected=128\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_118.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=701.288 0.493087\nTotal Stars=719 accepted=549 rejected=170\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=702.806 0.493087\nTotal Stars=480 accepted=360 rejected=120\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6268.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=703.229 0.493087\nTotal Stars=534 accepted=413 rejected=121\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6830.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=704.419 0.493087\nTotal Stars=434 accepted=361 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=704.597 0.493087\nTotal Stars=1359 accepted=1275 rejected=84\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_37.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=705.175 0.493087\nTotal Stars=363 accepted=281 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_258.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=706.205 0.493087\nTotal Stars=609 accepted=460 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1253.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=706.423 0.493087\nTotal Stars=399 accepted=312 rejected=87\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_78.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=707.135 0.493087\nTotal Stars=367 accepted=226 rejected=141\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2251.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=707.14 0.493087\nTotal Stars=605 accepted=459 rejected=146\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_105.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=707.708 0.493087\nTotal Stars=1139 accepted=924 rejected=215\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_41.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=708.351 0.493087\nTotal Stars=1239 accepted=1043 rejected=196\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0496.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=711.305 0.493087\nTotal Stars=720 accepted=471 rejected=249\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2581.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=711.48 0.493087\nTotal Stars=555 accepted=372 rejected=183\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1605.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=711.63 0.493087\nTotal Stars=512 accepted=410 rejected=102\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2453.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=711.843 0.493087\nTotal Stars=395 accepted=345 rejected=50\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=712.193 0.493087\nTotal Stars=427 accepted=339 rejected=88\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=712.326 0.493087\nTotal Stars=341 accepted=170 rejected=171\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2215.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=713.79 0.493087\nTotal Stars=707 accepted=593 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2482.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=713.933 0.493087\nTotal Stars=649 accepted=517 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPlatais_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=714.358 0.493087\nTotal Stars=1332 accepted=1332 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_27.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=715.103 0.493087\nTotal Stars=544 accepted=410 rejected=134\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_45.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=715.801 0.493087\nTotal Stars=336 accepted=224 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=716.188 0.493087\nTotal Stars=983 accepted=736 rejected=247\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_79.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=716.428 0.493087\nTotal Stars=1338 accepted=1070 rejected=268\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_217.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=717.072 0.493087\nTotal Stars=362 accepted=290 rejected=72\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_222.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=718.658 0.493087\nTotal Stars=270 accepted=219 rejected=51\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=721.482 0.493087\nTotal Stars=570 accepted=454 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_31.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=723.007 0.493087\nTotal Stars=351 accepted=203 rejected=148\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_176.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=723.792 0.493087\nTotal Stars=394 accepted=311 rejected=83\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=723.886 0.493087\nTotal Stars=1378 accepted=1378 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_87.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=724.484 0.493087\nTotal Stars=822 accepted=642 rejected=180\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2455.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=725.255 0.493087\nTotal Stars=406 accepted=317 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_156.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=726.697 0.493087\nTotal Stars=324 accepted=253 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=727.155 0.493087\nTotal Stars=458 accepted=341 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=728.449 0.493087\nTotal Stars=748 accepted=522 rejected=226\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_307.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=728.974 0.493087\nTotal Stars=678 accepted=495 rejected=183\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_130_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=730.757 0.493087\nTotal Stars=443 accepted=334 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_28.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=731.473 0.493087\nTotal Stars=371 accepted=300 rejected=71\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=731.593 0.493087\nTotal Stars=463 accepted=374 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=732.3 0.493087\nTotal Stars=358 accepted=253 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_366.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=732.411 0.493087\nTotal Stars=519 accepted=400 rejected=119\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=732.483 0.493087\nTotal Stars=824 accepted=663 rejected=161\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listESO_211_03.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=735.17 0.493087\nTotal Stars=312 accepted=258 rejected=54\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2448.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=735.314 0.493087\nTotal Stars=1120 accepted=906 rejected=214\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6827.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=735.374 0.493087\nTotal Stars=331 accepted=256 rejected=75\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_60.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=735.59 0.493087\nTotal Stars=382 accepted=305 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1805.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=736.075 0.493087\nTotal Stars=742 accepted=565 rejected=177\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2910.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=736.114 0.493087\nTotal Stars=988 accepted=811 rejected=177\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_58.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=736.579 0.493087\nTotal Stars=1388 accepted=1361 rejected=27\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_21.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=737.174 0.493087\nTotal Stars=1276 accepted=1092 rejected=184\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_220.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=738.88 0.493087\nTotal Stars=485 accepted=353 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6997.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=738.985 0.493087\nTotal Stars=879 accepted=666 rejected=213\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=739.103 0.493087\nTotal Stars=640 accepted=505 rejected=135\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=739.169 0.493087\nTotal Stars=446 accepted=295 rejected=151\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDC_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=741.727 0.493087\nTotal Stars=495 accepted=401 rejected=94\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1545.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=742.316 0.493087\nTotal Stars=870 accepted=695 rejected=175\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=742.519 0.493087\nTotal Stars=506 accepted=405 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_268.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=742.858 0.493087\nTotal Stars=374 accepted=310 rejected=64\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKoposov_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=743.234 0.493087\nTotal Stars=509 accepted=370 rejected=139\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1708.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=744.858 0.493087\nTotal Stars=889 accepted=599 rejected=290\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6425.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=744.967 0.493087\nTotal Stars=753 accepted=582 rejected=171\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=745.133 0.493087\nTotal Stars=399 accepted=300 rejected=99\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=746.218 0.493087\nTotal Stars=1108 accepted=938 rejected=170\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_58.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=746.302 0.493087\nTotal Stars=466 accepted=349 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2318.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=746.595 0.493087\nTotal Stars=708 accepted=530 rejected=178\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_28.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=746.72 0.493087\nTotal Stars=1247 accepted=1066 rejected=181\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_88.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=747.235 0.493087\nTotal Stars=1340 accepted=1084 rejected=256\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_95.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=748.7 0.493087\nTotal Stars=1317 accepted=1114 rejected=203\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0124.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=748.88 0.493087\nTotal Stars=450 accepted=347 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1586.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=749.177 0.493087\nTotal Stars=331 accepted=268 rejected=63\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_348.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=750.536 0.493087\nTotal Stars=1467 accepted=1467 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=751.416 0.493087\nTotal Stars=1214 accepted=1013 rejected=201\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_62.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=751.519 0.493087\nTotal Stars=1029 accepted=796 rejected=233\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_90.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=751.791 0.493087\nTotal Stars=421 accepted=328 rejected=93\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6704.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=751.799 0.493087\nTotal Stars=502 accepted=379 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_111.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=752.48 0.493087\nTotal Stars=589 accepted=466 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_58.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=755.359 0.493087\nTotal Stars=452 accepted=363 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_1378.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=755.707 0.493087\nTotal Stars=639 accepted=496 rejected=143\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=758.247 0.493087\nTotal Stars=422 accepted=324 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1582.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=759.037 0.493087\nTotal Stars=764 accepted=580 rejected=184\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7423.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=760.545 0.493087\nTotal Stars=426 accepted=323 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_202.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=760.723 0.493087\nTotal Stars=679 accepted=519 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_33.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=760.746 0.493087\nTotal Stars=357 accepted=287 rejected=70\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_381.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=761.081 0.493087\nTotal Stars=769 accepted=538 rejected=231\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_114.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=761.215 0.493087\nTotal Stars=1050 accepted=807 rejected=243\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_77.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=762.113 0.493087\nTotal Stars=1243 accepted=1047 rejected=196\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_140.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=762.344 0.493087\nTotal Stars=1467 accepted=1383 rejected=84\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=763.056 0.493087\nTotal Stars=686 accepted=497 rejected=189\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=763.36 0.493087\nTotal Stars=558 accepted=379 rejected=179\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2587.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=764.881 0.493087\nTotal Stars=444 accepted=338 rejected=106\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_581.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=765.297 0.493087\nTotal Stars=550 accepted=397 rejected=153\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5460.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=766.262 0.493087\nTotal Stars=913 accepted=753 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1502.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=767.213 0.493087\nTotal Stars=1254 accepted=1041 rejected=213\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2414.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=767.643 0.493087\nTotal Stars=676 accepted=481 rejected=195\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7788.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=770.45 0.493087\nTotal Stars=558 accepted=385 rejected=173\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=771.142 0.493087\nTotal Stars=667 accepted=531 rejected=136\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=771.729 0.493087\nTotal Stars=836 accepted=670 rejected=166\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=771.862 0.493087\nTotal Stars=385 accepted=330 rejected=55\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_659.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=772.292 0.493087\nTotal Stars=505 accepted=391 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_30.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=772.405 0.493087\nTotal Stars=699 accepted=555 rejected=144\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_111.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=773.676 0.493087\nTotal Stars=1459 accepted=1438 rejected=21\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=774.729 0.493087\nTotal Stars=749 accepted=610 rejected=139\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2254.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=775.416 0.493087\nTotal Stars=514 accepted=406 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2849.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=776.78 0.493087\nTotal Stars=341 accepted=273 rejected=68\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDolidze_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=778.229 0.493087\nTotal Stars=537 accepted=429 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6823.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=779.971 0.493087\nTotal Stars=756 accepted=588 rejected=168\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_107.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=780.948 0.493087\nTotal Stars=1138 accepted=880 rejected=258\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6910.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=781.882 0.493087\nTotal Stars=748 accepted=578 rejected=170\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_164.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=782.157 0.493087\nTotal Stars=361 accepted=312 rejected=49\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_957.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=783.094 0.493087\nTotal Stars=712 accepted=559 rejected=153\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7092.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=783.286 0.493087\nTotal Stars=1284 accepted=1154 rejected=130\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=785.618 0.493087\nTotal Stars=846 accepted=678 rejected=168\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_221.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=785.806 0.493087\nTotal Stars=944 accepted=739 rejected=205\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_67.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=786.701 0.493087\nTotal Stars=517 accepted=371 rejected=146\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2192.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=787.913 0.493087\nTotal Stars=436 accepted=310 rejected=126\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0942.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=788.501 0.493087\nTotal Stars=500 accepted=383 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHarvard_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=788.602 0.493087\nTotal Stars=1054 accepted=841 rejected=213\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2671.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=788.785 0.493087\nTotal Stars=602 accepted=471 rejected=131\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=788.988 0.493087\nTotal Stars=1369 accepted=1131 rejected=238\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2362.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=789.533 0.493087\nTotal Stars=1476 accepted=1339 rejected=137\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2546.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=789.544 0.493087\nTotal Stars=935 accepted=731 rejected=204\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2571.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=790.052 0.493087\nTotal Stars=934 accepted=746 rejected=188\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6568.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=790.362 0.493087\nTotal Stars=857 accepted=651 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_130.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=790.833 0.493087\nTotal Stars=783 accepted=610 rejected=173\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_98.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=791.214 0.493087\nTotal Stars=431 accepted=286 rejected=145\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=791.454 0.493087\nTotal Stars=354 accepted=277 rejected=77\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=792.13 0.493087\nTotal Stars=450 accepted=358 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_116.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=792.83 0.493087\nTotal Stars=362 accepted=297 rejected=65\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1893.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=793.289 0.493087\nTotal Stars=921 accepted=706 rejected=215\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2286.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=793.799 0.493087\nTotal Stars=563 accepted=456 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6735.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=794.635 0.493087\nTotal Stars=469 accepted=403 rejected=66\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2264.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=796.213 0.493087\nTotal Stars=1548 accepted=1537 rejected=11\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_81.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=797.018 0.493087\nTotal Stars=399 accepted=314 rejected=85\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_4665.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=799.616 0.493087\nTotal Stars=1493 accepted=1490 rejected=3\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4052.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=799.747 0.493087\nTotal Stars=495 accepted=382 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7031.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=802.253 0.493087\nTotal Stars=592 accepted=469 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=803.924 0.493087\nTotal Stars=431 accepted=308 rejected=123\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_436.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=803.954 0.493087\nTotal Stars=425 accepted=352 rejected=73\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5715.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=806.152 0.493087\nTotal Stars=510 accepted=406 rejected=104\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=806.573 0.493087\nTotal Stars=416 accepted=324 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_147.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=807.156 0.493087\nTotal Stars=1269 accepted=1029 rejected=240\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTombaugh_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=807.313 0.493087\nTotal Stars=471 accepted=392 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_15.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=809.583 0.493087\nTotal Stars=533 accepted=408 rejected=125\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1778.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=810.338 0.493087\nTotal Stars=623 accepted=460 rejected=163\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSAI_132.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=810.842 0.493087\nTotal Stars=439 accepted=330 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=813.719 0.493087\nTotal Stars=918 accepted=697 rejected=221\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=814.402 0.493087\nTotal Stars=1103 accepted=827 rejected=276\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_36.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=814.862 0.493087\nTotal Stars=421 accepted=262 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2635.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=815.337 0.493087\nTotal Stars=474 accepted=361 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=817.633 0.493087\nTotal Stars=424 accepted=316 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=820.141 0.493087\nTotal Stars=1435 accepted=1206 rejected=229\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_350.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=820.768 0.493087\nTotal Stars=1333 accepted=1083 rejected=250\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2428.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=822.337 0.493087\nTotal Stars=697 accepted=560 rejected=137\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_79.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=824.578 0.493087\nTotal Stars=420 accepted=329 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_70.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=825.076 0.493087\nTotal Stars=417 accepted=230 rejected=187\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTeutsch_84.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=826.28 0.493087\nTotal Stars=487 accepted=368 rejected=119\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=827.034 0.493087\nTotal Stars=1232 accepted=1010 rejected=222\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7128.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=827.614 0.493087\nTotal Stars=526 accepted=370 rejected=156\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=827.734 0.493087\nTotal Stars=587 accepted=428 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=829.225 0.493087\nTotal Stars=488 accepted=381 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6216.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=829.476 0.493087\nTotal Stars=801 accepted=619 rejected=182\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_394.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=830.731 0.493087\nTotal Stars=1140 accepted=921 rejected=219\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6318.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=831.497 0.493087\nTotal Stars=568 accepted=430 rejected=138\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=831.718 0.493087\nTotal Stars=1352 accepted=1171 rejected=181\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=831.829 0.493087\nTotal Stars=459 accepted=349 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3590.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=835.098 0.493087\nTotal Stars=596 accepted=441 rejected=155\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_29.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=838.242 0.493087\nTotal Stars=661 accepted=548 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listWesterlund_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=838.265 0.493087\nTotal Stars=651 accepted=487 rejected=164\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6633.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=839.635 0.493087\nTotal Stars=1363 accepted=1109 rejected=254\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=839.658 0.493087\nTotal Stars=1585 accepted=1585 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=840.495 0.493087\nTotal Stars=590 accepted=480 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=842.491 0.493087\nTotal Stars=399 accepted=320 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6793.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=845.16 0.493087\nTotal Stars=1142 accepted=865 rejected=277\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=845.181 0.493087\nTotal Stars=671 accepted=519 rejected=152\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2432.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=845.642 0.493087\nTotal Stars=599 accepted=473 rejected=126\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6583.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=846.924 0.493087\nTotal Stars=553 accepted=435 rejected=118\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=847.148 0.493087\nTotal Stars=524 accepted=403 rejected=121\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_29.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=847.637 0.493087\nTotal Stars=461 accepted=379 rejected=82\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_103.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=848.915 0.493087\nTotal Stars=507 accepted=372 rejected=135\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listvdBergh_92.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=850.03 0.493087\nTotal Stars=1000 accepted=813 rejected=187\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=850.618 0.493087\nTotal Stars=1597 accepted=1406 rejected=191\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_89.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=850.687 0.493087\nTotal Stars=417 accepted=312 rejected=105\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_38.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=852.304 0.493087\nTotal Stars=675 accepted=491 rejected=184\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0336.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=852.999 0.493087\nTotal Stars=519 accepted=395 rejected=124\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_72.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=853.198 0.493087\nTotal Stars=562 accepted=407 rejected=155\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2335.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=856.426 0.493087\nTotal Stars=663 accepted=539 rejected=124\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=857.255 0.493087\nTotal Stars=410 accepted=350 rejected=60\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_113.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=858.158 0.493087\nTotal Stars=1387 accepted=1125 rejected=262\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=858.288 0.493087\nTotal Stars=559 accepted=452 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_128.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=858.672 0.493087\nTotal Stars=562 accepted=375 rejected=187\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2232.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=858.889 0.493087\nTotal Stars=1646 accepted=1618 rejected=28\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_87.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=859.196 0.493087\nTotal Stars=768 accepted=579 rejected=189\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMamajek_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=859.849 0.493087\nTotal Stars=1187 accepted=963 rejected=224\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1369.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=859.934 0.493087\nTotal Stars=476 accepted=376 rejected=100\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7790.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=860.654 0.493087\nTotal Stars=512 accepted=415 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=861.273 0.493087\nTotal Stars=594 accepted=419 rejected=175\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6152.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=862.158 0.493087\nTotal Stars=660 accepted=498 rejected=162\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6400.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=863.247 0.493087\nTotal Stars=770 accepted=595 rejected=175\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=864.282 0.493087\nTotal Stars=427 accepted=297 rejected=130\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6694.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=865.629 0.493087\nTotal Stars=603 accepted=489 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5168.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=866.858 0.493087\nTotal Stars=484 accepted=383 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_277.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=867.541 0.493087\nTotal Stars=660 accepted=523 rejected=137\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=868.026 0.493087\nTotal Stars=426 accepted=345 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listJuchert_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=869.37 0.493087\nTotal Stars=486 accepted=358 rejected=128\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4852.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=872.204 0.493087\nTotal Stars=766 accepted=581 rejected=185\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7235.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=872.761 0.493087\nTotal Stars=548 accepted=385 rejected=163\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_164.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=874.052 0.493087\nTotal Stars=1677 accepted=1671 rejected=6\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_44.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=875.995 0.493087\nTotal Stars=436 accepted=329 rejected=107\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2670.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=877.779 0.493087\nTotal Stars=796 accepted=628 rejected=168\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHaffner_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=877.969 0.493087\nTotal Stars=1522 accepted=1308 rejected=214\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4349.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=879.496 0.493087\nTotal Stars=616 accepted=483 rejected=133\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPlatais_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=879.832 0.493087\nTotal Stars=1682 accepted=1682 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTombaugh_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=881.85 0.493087\nTotal Stars=547 accepted=429 rejected=118\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_91.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=883.529 0.493087\nTotal Stars=897 accepted=671 rejected=226\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_115.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=884.535 0.493087\nTotal Stars=795 accepted=597 rejected=198\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1193.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=884.717 0.493087\nTotal Stars=403 accepted=261 rejected=142\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1798.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=885.262 0.493087\nTotal Stars=445 accepted=295 rejected=150\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_43.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=886.083 0.493087\nTotal Stars=405 accepted=326 rejected=79\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2353.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=886.194 0.493087\nTotal Stars=1355 accepted=1137 rejected=218\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6531.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=890.721 0.493087\nTotal Stars=1373 accepted=1166 rejected=207\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2669.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=892.293 0.493087\nTotal Stars=914 accepted=656 rejected=258\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=894.604 0.493087\nTotal Stars=438 accepted=338 rejected=100\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=901.712 0.493087\nTotal Stars=1300 accepted=1014 rejected=286\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_121.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=902.541 0.493087\nTotal Stars=1050 accepted=844 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6204.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=905.196 0.493087\nTotal Stars=923 accepted=669 rejected=254\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2391.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=906.322 0.493087\nTotal Stars=1726 accepted=1726 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2309.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=906.7 0.493087\nTotal Stars=628 accepted=484 rejected=144\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=909.692 0.493087\nTotal Stars=1765 accepted=1765 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5999.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=912.382 0.493087\nTotal Stars=525 accepted=428 rejected=97\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=913.591 0.493087\nTotal Stars=517 accepted=460 rejected=57\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listDias_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=914.344 0.493087\nTotal Stars=519 accepted=430 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_37.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=914.546 0.493087\nTotal Stars=571 accepted=444 rejected=127\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_145.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=914.596 0.493087\nTotal Stars=1139 accepted=915 rejected=224\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=914.719 0.493087\nTotal Stars=622 accepted=467 rejected=155\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_108.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=917.083 0.493087\nTotal Stars=824 accepted=659 rejected=165\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6728.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=918.257 0.493087\nTotal Stars=656 accepted=497 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0342.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=918.338 0.493087\nTotal Stars=568 accepted=460 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4337.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=918.664 0.493087\nTotal Stars=573 accepted=400 rejected=173\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2547.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=923.796 0.493087\nTotal Stars=1697 accepted=1411 rejected=286\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6451.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=924.975 0.493087\nTotal Stars=527 accepted=441 rejected=86\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCzernik_41.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=928.121 0.493087\nTotal Stars=558 accepted=412 rejected=146\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=928.872 0.493087\nTotal Stars=1291 accepted=977 rejected=314\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCep_OB5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=930.318 0.493087\nTotal Stars=572 accepted=456 rejected=116\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6664.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=930.991 0.493087\nTotal Stars=622 accepted=500 rejected=122\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1662.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=932.576 0.493087\nTotal Stars=1459 accepted=1188 rejected=271\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_609.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=933.985 0.493087\nTotal Stars=466 accepted=331 rejected=135\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_59.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=935.188 0.493087\nTotal Stars=1906 accepted=1600 rejected=306\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_752.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=938.428 0.493087\nTotal Stars=1416 accepted=1181 rejected=235\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6167.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=940.026 0.493087\nTotal Stars=739 accepted=626 rejected=113\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_197.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=941.19 0.493087\nTotal Stars=1371 accepted=1156 rejected=215\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_24.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=943.5 0.493087\nTotal Stars=418 accepted=286 rejected=132\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_68.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=947.325 0.493087\nTotal Stars=575 accepted=446 rejected=129\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2266.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=952.146 0.493087\nTotal Stars=531 accepted=392 rejected=139\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRoslund_6.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=952.462 0.493087\nTotal Stars=1616 accepted=1422 rejected=194\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0088.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=953.232 0.493087\nTotal Stars=455 accepted=393 rejected=62\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6087.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=953.325 0.493087\nTotal Stars=1129 accepted=805 rejected=324\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=953.861 0.493087\nTotal Stars=546 accepted=348 rejected=198\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2262.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=956.354 0.493087\nTotal Stars=479 accepted=362 rejected=117\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2509.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=956.594 0.493087\nTotal Stars=617 accepted=453 rejected=164\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5662.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=959.09 0.493087\nTotal Stars=1143 accepted=898 rejected=245\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2425.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=961.671 0.493087\nTotal Stars=532 accepted=335 rejected=197\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=962.155 0.493087\nTotal Stars=1143 accepted=934 rejected=209\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2383.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=964.737 0.493087\nTotal Stars=536 accepted=427 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6834.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=965.57 0.493087\nTotal Stars=529 accepted=448 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_17.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=965.861 0.493087\nTotal Stars=566 accepted=341 rejected=225\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=967.397 0.493087\nTotal Stars=593 accepted=434 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTombaugh_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=968.119 0.493087\nTotal Stars=393 accepted=301 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6756.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=968.25 0.493087\nTotal Stars=511 accepted=413 rejected=98\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7245.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=968.653 0.493087\nTotal Stars=492 accepted=401 rejected=91\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCep_OB3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=969.591 0.493087\nTotal Stars=1456 accepted=1167 rejected=289\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6383.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=976.431 0.493087\nTotal Stars=1879 accepted=1568 rejected=311\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=976.488 0.493087\nTotal Stars=552 accepted=376 rejected=176\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2818.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=976.608 0.493087\nTotal Stars=505 accepted=391 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6709.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=976.854 0.493087\nTotal Stars=990 accepted=770 rejected=220\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listASCC_11.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=977.87 0.493087\nTotal Stars=1171 accepted=940 rejected=231\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2354.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=978.246 0.493087\nTotal Stars=868 accepted=632 rejected=236\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6416.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=979.369 0.493087\nTotal Stars=993 accepted=768 rejected=225\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHogg_4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=981.743 0.493087\nTotal Stars=482 accepted=408 rejected=74\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1434.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=983.208 0.493087\nTotal Stars=523 accepted=420 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6404.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=983.823 0.493087\nTotal Stars=535 accepted=451 rejected=84\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6755.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=984.039 0.493087\nTotal Stars=635 accepted=527 rejected=108\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2489.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=984.33 0.493087\nTotal Stars=860 accepted=662 rejected=198\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=984.456 0.493087\nTotal Stars=628 accepted=488 rejected=140\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3960.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=986.266 0.493087\nTotal Stars=612 accepted=446 rejected=166\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_13.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=986.863 0.493087\nTotal Stars=542 accepted=414 rejected=128\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2355.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=989.079 0.493087\nTotal Stars=746 accepted=550 rejected=196\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_272.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=990.486 0.493087\nTotal Stars=888 accepted=682 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1907.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=991.385 0.493087\nTotal Stars=881 accepted=674 rejected=207\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2395.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=993.255 0.493087\nTotal Stars=1780 accepted=1517 rejected=263\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2451B.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=993.335 0.493087\nTotal Stars=1674 accepted=1450 rejected=224\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1960.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=993.433 0.493087\nTotal Stars=1407 accepted=1151 rejected=256\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=998.331 0.493087\nTotal Stars=1893 accepted=1603 rejected=290\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6811.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=999.108 0.493087\nTotal Stars=1013 accepted=733 rejected=280\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2324.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1004.24 0.493087\nTotal Stars=529 accepted=440 rejected=89\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_101.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1005.54 0.493087\nTotal Stars=510 accepted=418 rejected=92\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2602.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1007.28 0.493087\nTotal Stars=1912 accepted=1912 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7209.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1007.66 0.493087\nTotal Stars=1013 accepted=712 rejected=301\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listAlessi_12.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1008.72 0.493087\nTotal Stars=1898 accepted=1586 rejected=312\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3603.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1008.88 0.493087\nTotal Stars=461 accepted=362 rejected=99\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7243.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1013.19 0.493087\nTotal Stars=1284 accepted=1021 rejected=263\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7082.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1013.27 0.493087\nTotal Stars=897 accepted=715 rejected=182\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listFSR_0133.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1017.88 0.493087\nTotal Stars=567 accepted=466 rejected=101\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPer_OB2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1018.58 0.493087\nTotal Stars=1946 accepted=1946 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_18.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1022.47 0.493087\nTotal Stars=469 accepted=257 rejected=212\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4609.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1027.79 0.493087\nTotal Stars=884 accepted=662 rejected=222\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_14.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1029.14 0.493087\nTotal Stars=1079 accepted=873 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7510.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1033.46 0.493087\nTotal Stars=555 accepted=474 rejected=81\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6025.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1033.96 0.493087\nTotal Stars=1355 accepted=1014 rejected=341\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1664.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1038.27 0.493087\nTotal Stars=902 accepted=688 rejected=214\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_359.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1038.99 0.493087\nTotal Stars=1942 accepted=1663 rejected=279\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1528.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1041.02 0.493087\nTotal Stars=1235 accepted=888 rejected=347\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7419.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1041.11 0.493087\nTotal Stars=623 accepted=435 rejected=188\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2451A.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1041.75 0.493087\nTotal Stars=1989 accepted=1989 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2627.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1042.42 0.493087\nTotal Stars=807 accepted=562 rejected=245\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6005.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1043.07 0.493087\nTotal Stars=547 accepted=444 rejected=103\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6208.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1043.56 0.493087\nTotal Stars=1046 accepted=694 rejected=352\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1513.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1047.23 0.493087\nTotal Stars=957 accepted=782 rejected=175\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_8.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1048.5 0.493087\nTotal Stars=1165 accepted=929 rejected=236\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_135.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1051.36 0.493087\nTotal Stars=2003 accepted=2003 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_68.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1054.51 0.493087\nTotal Stars=573 accepted=406 rejected=167\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2527.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1055.72 0.493087\nTotal Stars=1385 accepted=1079 rejected=306\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listWesterlund_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1055.73 0.493087\nTotal Stars=698 accepted=512 rejected=186\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1342.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1057.84 0.493087\nTotal Stars=1439 accepted=1108 rejected=331\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_654.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1059.35 0.493087\nTotal Stars=902 accepted=625 rejected=277\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4103.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1060.36 0.493087\nTotal Stars=742 accepted=580 rejected=162\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6645.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1062.25 0.493087\nTotal Stars=810 accepted=587 rejected=223\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3293.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1063.48 0.493087\nTotal Stars=1143 accepted=919 rejected=224\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_7.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1064.62 0.493087\nTotal Stars=628 accepted=462 rejected=166\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listSkiff_J0058+68.4.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1067.84 0.493087\nTotal Stars=674 accepted=499 rejected=175\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBlanco_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1068.88 0.493087\nTotal Stars=2035 accepted=2035 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1848.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1069.4 0.493087\nTotal Stars=1107 accepted=899 rejected=208\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_85.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1070.29 0.493087\nTotal Stars=467 accepted=368 rejected=99\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6611.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1071.66 0.493087\nTotal Stars=1604 accepted=1336 rejected=268\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6802.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1071.69 0.493087\nTotal Stars=570 accepted=443 rejected=127\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_99.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1071.72 0.493087\nTotal Stars=1863 accepted=1577 rejected=286\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2658.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1072.29 0.493087\nTotal Stars=509 accepted=400 rejected=109\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_129.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1072.47 0.493087\nTotal Stars=846 accepted=682 rejected=164\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1027.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1074.26 0.493087\nTotal Stars=1091 accepted=799 rejected=292\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_121.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1075.81 0.493087\nTotal Stars=759 accepted=585 rejected=174\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5316.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1075.95 0.493087\nTotal Stars=956 accepted=691 rejected=265\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7142.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1076.27 0.493087\nTotal Stars=654 accepted=456 rejected=198\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_32.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1080.66 0.493087\nTotal Stars=678 accepted=390 rejected=288\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6253.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1081.89 0.493087\nTotal Stars=730 accepted=413 rejected=317\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listHarvard_16.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1085.24 0.493087\nTotal Stars=962 accepted=712 rejected=250\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2281.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1086.17 0.493087\nTotal Stars=1615 accepted=1372 rejected=243\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_112.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1093.51 0.493087\nTotal Stars=690 accepted=541 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2660.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1093.53 0.493087\nTotal Stars=611 accepted=452 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_23.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1097.54 0.493087\nTotal Stars=623 accepted=484 rejected=139\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2423.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1107.29 0.493087\nTotal Stars=1306 accepted=900 rejected=406\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7762.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1107.71 0.493087\nTotal Stars=1144 accepted=853 rejected=291\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_463.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1108.12 0.493087\nTotal Stars=1253 accepted=987 rejected=266\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2421.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1110.23 0.493087\nTotal Stars=843 accepted=593 rejected=250\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2236.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1111.22 0.493087\nTotal Stars=685 accepted=566 rejected=119\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3496.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1115.38 0.493087\nTotal Stars=608 accepted=498 rejected=110\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2488.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1117.84 0.493087\nTotal Stars=991 accepted=748 rejected=243\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2420.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1118.59 0.493087\nTotal Stars=704 accepted=485 rejected=219\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBH_140.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1128.43 0.493087\nTotal Stars=484 accepted=305 rejected=179\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2422.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1131.64 0.493087\nTotal Stars=1886 accepted=1614 rejected=272\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5381.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1132.92 0.493087\nTotal Stars=651 accepted=561 rejected=90\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5925.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1133.12 0.493087\nTotal Stars=1002 accepted=763 rejected=239\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listLynga_9.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1135.03 0.493087\nTotal Stars=703 accepted=549 rejected=154\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1135.63 0.493087\nTotal Stars=542 accepted=397 rejected=145\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2301.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1136.17 0.493087\nTotal Stars=1598 accepted=1137 rejected=461\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1396.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1137.08 0.493087\nTotal Stars=2238 accepted=2126 rejected=112\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1817.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1139.17 0.493087\nTotal Stars=874 accepted=698 rejected=176\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6193.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1139.25 0.493087\nTotal Stars=1854 accepted=1534 rejected=320\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTombaugh_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1140.62 0.493087\nTotal Stars=894 accepted=677 rejected=217\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1912.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1142.14 0.493087\nTotal Stars=1168 accepted=872 rejected=296\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2345.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1142.89 0.493087\nTotal Stars=750 accepted=648 rejected=102\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_10.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1144.36 0.493087\nTotal Stars=2182 accepted=2137 rejected=45\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2548.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1149.85 0.493087\nTotal Stars=1238 accepted=1014 rejected=224\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_884.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1163.9 0.493087\nTotal Stars=1095 accepted=801 rejected=294\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_4756.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1169.09 0.493087\nTotal Stars=1627 accepted=1397 rejected=230\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6603.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1172.22 0.493087\nTotal Stars=659 accepted=545 rejected=114\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_1311.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1172.62 0.493087\nTotal Stars=520 accepted=391 rejected=129\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_361.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1172.95 0.493087\nTotal Stars=648 accepted=501 rejected=147\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_105.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1174.86 0.493087\nTotal Stars=714 accepted=580 rejected=134\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5823.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1176.12 0.493087\nTotal Stars=810 accepted=616 rejected=194\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6281.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1176.31 0.493087\nTotal Stars=1601 accepted=1392 rejected=209\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2243.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1177.87 0.493087\nTotal Stars=615 accepted=456 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_4725.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1178.51 0.493087\nTotal Stars=1756 accepted=1430 rejected=326\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2539.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1182.32 0.493087\nTotal Stars=1028 accepted=785 rejected=243\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_101.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1184.37 0.493087\nTotal Stars=819 accepted=660 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6242.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1185.55 0.493087\nTotal Stars=1253 accepted=1028 rejected=225\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6192.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1192.43 0.493087\nTotal Stars=922 accepted=754 rejected=168\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_559.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1198.71 0.493087\nTotal Stars=797 accepted=615 rejected=182\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_39.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1202.45 0.493087\nTotal Stars=626 accepted=385 rejected=241\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2439.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1204.24 0.493087\nTotal Stars=726 accepted=512 rejected=214\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6649.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1204.26 0.493087\nTotal Stars=827 accepted=657 rejected=170\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2204.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1205.37 0.493087\nTotal Stars=613 accepted=440 rejected=173\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1039.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1205.93 0.493087\nTotal Stars=1736 accepted=1502 rejected=234\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCyg_OB2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1208.05 0.493087\nTotal Stars=1570 accepted=1280 rejected=290\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6405.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1209.08 0.493087\nTotal Stars=2185 accepted=1834 rejected=351\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_71.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1213.6 0.493087\nTotal Stars=841 accepted=635 rejected=206\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1245.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1213.72 0.493087\nTotal Stars=711 accepted=552 rejected=159\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6940.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1224.91 0.493087\nTotal Stars=1223 accepted=872 rejected=351\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_457.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1226.47 0.493087\nTotal Stars=892 accepted=626 rejected=266\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6871.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1230.26 0.493087\nTotal Stars=1542 accepted=1219 rejected=323\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4815.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1230.66 0.493087\nTotal Stars=671 accepted=530 rejected=141\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listBerkeley_53.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1234.61 0.493087\nTotal Stars=618 accepted=484 rejected=134\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_1647.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1236.76 0.493087\nTotal Stars=1679 accepted=1415 rejected=264\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7086.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1250.84 0.493087\nTotal Stars=974 accepted=764 rejected=210\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2244.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1250.92 0.493087\nTotal Stars=1869 accepted=1550 rejected=319\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_663.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1255.3 0.493087\nTotal Stars=1013 accepted=684 rejected=329\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5617.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1257.53 0.493087\nTotal Stars=874 accepted=713 rejected=161\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_19.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1258.21 0.493087\nTotal Stars=786 accepted=448 rejected=338\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2287.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1258.59 0.493087\nTotal Stars=1713 accepted=1306 rejected=407\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_4755.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1270.36 0.493087\nTotal Stars=1140 accepted=856 rejected=284\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6231.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1280.27 0.493087\nTotal Stars=1488 accepted=1229 rejected=259\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6939.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1289.94 0.493087\nTotal Stars=891 accepted=668 rejected=223\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3766.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1292.54 0.493087\nTotal Stars=919 accepted=698 rejected=221\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_5822.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1293.85 0.493087\nTotal Stars=1532 accepted=1142 rejected=390\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_69.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1295.45 0.493087\nTotal Stars=2436 accepted=2415 rejected=21\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7044.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1298.7 0.493087\nTotal Stars=718 accepted=486 rejected=232\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_25.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1303.57 0.493087\nTotal Stars=804 accepted=644 rejected=160\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listKing_1.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1308.37 0.493087\nTotal Stars=1018 accepted=590 rejected=428\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2682.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1309.92 0.493087\nTotal Stars=1543 accepted=1053 rejected=490\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2360.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1314.81 0.493087\nTotal Stars=1337 accepted=1068 rejected=269\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2632.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1339.65 0.493087\nTotal Stars=2568 accepted=2314 rejected=254\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2112.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1339.89 0.493087\nTotal Stars=1349 accepted=948 rejected=401\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_869.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1343.15 0.493087\nTotal Stars=1183 accepted=889 rejected=294\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_166.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1351.65 0.493087\nTotal Stars=622 accepted=473 rejected=149\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listRuprecht_171.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1364.19 0.493087\nTotal Stars=1209 accepted=753 rejected=456\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2447.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1376.86 0.493087\nTotal Stars=1420 accepted=1096 rejected=324\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1381.68 0.493087\nTotal Stars=2607 accepted=2606 rejected=1\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6494.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1385.48 0.493087\nTotal Stars=1631 accepted=1366 rejected=265\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2516.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1389.7 0.493087\nTotal Stars=2066 accepted=1764 rejected=302\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6134.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1391.92 0.493087\nTotal Stars=1418 accepted=943 rejected=475\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2194.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1406.18 0.493087\nTotal Stars=776 accepted=631 rejected=145\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_66.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1427.87 0.493087\nTotal Stars=727 accepted=470 rejected=257\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2141.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1429.07 0.493087\nTotal Stars=729 accepted=521 rejected=208\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_4651.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1430.77 0.493087\nTotal Stars=1444 accepted=1102 rejected=342\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_188.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1453.92 0.493087\nTotal Stars=1128 accepted=667 rejected=461\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2323.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1455.92 0.493087\nTotal Stars=1671 accepted=1276 rejected=395\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_20.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1464.08 0.493087\nTotal Stars=742 accepted=520 rejected=222\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listIC_2714.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1466.38 0.493087\nTotal Stars=1149 accepted=957 rejected=192\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6475.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1468.71 0.493087\nTotal Stars=2801 accepted=2503 rejected=298\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_110.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1473.15 0.493087\nTotal Stars=1010 accepted=754 rejected=256\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6259.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1477.36 0.493087\nTotal Stars=896 accepted=693 rejected=203\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listMelotte_22.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1478.03 0.493087\nTotal Stars=2790 accepted=2790 rejected=0\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6067.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1498.62 0.493087\nTotal Stars=1040 accepted=823 rejected=217\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7654.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1504.48 0.493087\nTotal Stars=1540 accepted=1119 rejected=421\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listPismis_3.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1507.53 0.493087\nTotal Stars=1031 accepted=781 rejected=250\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listStock_2.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1537.53 0.493087\nTotal Stars=2461 accepted=2160 rejected=301\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3114.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1542.01 0.493087\nTotal Stars=1583 accepted=1217 rejected=366\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2168.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1544.62 0.493087\nTotal Stars=1846 accepted=1410 rejected=436\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6124.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1568.93 0.493087\nTotal Stars=2096 accepted=1684 rejected=412\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2158.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1589.84 0.493087\nTotal Stars=824 accepted=553 rejected=271\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6705.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1612.86 0.493087\nTotal Stars=994 accepted=792 rejected=202\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2506.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1613.29 0.493087\nTotal Stars=914 accepted=693 rejected=221\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_6819.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1629.38 0.493087\nTotal Stars=1005 accepted=696 rejected=309\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2099.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1671.94 0.493087\nTotal Stars=1479 accepted=1103 rejected=376\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2437.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1689.92 0.493087\nTotal Stars=1507 accepted=1149 rejected=358\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listCollinder_261.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1735.4 0.493087\nTotal Stars=960 accepted=527 rejected=433\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_3532.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1742.22 0.493087\nTotal Stars=2396 accepted=2061 rejected=335\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_2477.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1837.02 0.493087\nTotal Stars=1559 accepted=1186 rejected=373\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listTrumpler_5.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=1862.51 0.493087\nTotal Stars=1082 accepted=700 rejected=382\n-----------Done---------------\n------------------------------\nnbody1/cluster/cluster_listNGC_7789.ebf Sat No=0\nParticles=1000\nSatellite Info\nSatellite Initializing ....... Done\nParticles=1000 Mass=2045.84 0.493087\nTotal Stars=1419 accepted=985 rejected=434\n-----------Done---------------\nTotal stars written 541004 \nFile written- ../output/Clusters_1//nbody.ebf\nCalulating magnitudes................\nReading Isochrones from dir- /home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\nzsol=0.0152\n/home/rybizki/Programme/GalaxiaData/Isochrones/padova/parsec1/GAIADR3\n13275 75 177\nIsochrone Grid Size: (Age bins=177,Feh bins=75,Alpha bins=1)\nTime Isochrone Reading 1.52286 \ngaia_g\ngaia_bpbr\ngaia_bpft\ngaia_rp\ngaia_rvs\nCalulating Extinction................\nTime for extinction calculation 0.546223 \nTotal Time= 5.28214 \n'
error: b''
########################################################################################
############################# GALAXIA OUTPUT END ##################
########################################################################################
541004
('rad', 'teff', 'vx', 'vy', 'vz', 'pz', 'px', 'py', 'feh', 'exbv_schlegel', 'lum', 'glon', 'glat', 'smass', 'age', 'grav', 'gaia_g', 'gaia_bpft', 'gaia_bpbr', 'gaia_rp', 'gaia_rvs', 'popid', 'mact')
converting to npy and appending ra and dec took 2.8 sec
0 541004
converting time and applying extinction map for 541004 sources in nside = 512 took 2.3 sec
indexing and remapping to isochrones took 5.6 sec
calculating extinction curve for all bands took 13.9 sec
541004
441697
calculated healpix
calculated pmdec pmra and rv
cleaning of data took 3.5 sec
/home/rybizki/Desktop/Galaxia_wrap-master/library/defaults.py:9: UserWarning:
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:
File "/home/rybizki/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/rybizki/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2802, in run_ast_nodes
if self.run_code(code, result):
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-2d703e2efe08>", line 4, in <module>
get_ipython().magic('pylab inline')
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2146, in magic
return self.run_line_magic(magic_name, magic_arg_s)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2067, in run_line_magic
result = fn(*args,**kwargs)
File "<decorator-gen-108>", line 2, in pylab
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/magic.py", line 187, in <lambda>
call = lambda f, *a, **k: f(*a, **k)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/magics/pylab.py", line 155, in pylab
gui, backend, clobbered = self.shell.enable_pylab(args.gui, import_all=import_all)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2969, in enable_pylab
gui, backend = self.enable_matplotlib(gui)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2930, in enable_matplotlib
pt.activate_matplotlib(backend)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/IPython/core/pylabtools.py", line 307, in activate_matplotlib
matplotlib.pyplot.switch_backend(backend)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py", line 231, in switch_backend
matplotlib.use(newbackend, warn=False, force=True)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py", line 1422, in use
reload(sys.modules['matplotlib.backends'])
File "/home/rybizki/anaconda3/lib/python3.6/importlib/__init__.py", line 166, in reload
_bootstrap._exec(spec, module)
File "/home/rybizki/anaconda3/lib/python3.6/site-packages/matplotlib/backends/__init__.py", line 16, in <module>
line for line in traceback.format_stack()
matplotlib.use('Agg') ## use a non-interactive Agg background
total number of stars = 441697
0.0
441697.0
33681395.7873
plotting time took 2.5 sec
Total time in minutes: 0.6
```python
```
|
jan-rybizkiREPO_NAMEGalaxia_wrapPATH_START.@Galaxia_wrap_extracted@Galaxia_wrap-master@notebook@.ipynb_checkpoints@[7]cluster mockup-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "utils.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/prompt-toolkit/py3/prompt_toolkit/filters/utils.py",
"type": "Python"
}
|
from __future__ import annotations
from .base import Always, Filter, FilterOrBool, Never
__all__ = [
"to_filter",
"is_true",
]
_always = Always()
_never = Never()
_bool_to_filter: dict[bool, Filter] = {
True: _always,
False: _never,
}
def to_filter(bool_or_filter: FilterOrBool) -> Filter:
"""
Accept both booleans and Filters as input and
turn it into a Filter.
"""
if isinstance(bool_or_filter, bool):
return _bool_to_filter[bool_or_filter]
if isinstance(bool_or_filter, Filter):
return bool_or_filter
raise TypeError(f"Expecting a bool or a Filter instance. Got {bool_or_filter!r}")
def is_true(value: FilterOrBool) -> bool:
"""
Test whether `value` is True. In case of a Filter, call it.
:param value: Boolean or `Filter` instance.
"""
return to_filter(value)()
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@prompt-toolkit@py3@prompt_toolkit@filters@utils.py@.PATH_END.py
|
{
"filename": "inspect_ar_chi_cost_results.ipynb",
"repo_name": "tcallister/autoregressive-bbh-inference",
"repo_path": "autoregressive-bbh-inference_extracted/autoregressive-bbh-inference-main/data/inspect_ar_chi_cost_results.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import arviz as az
import matplotlib.pylab as plt
import matplotlib as mpl
mpl.style.use("./../figures/plotting.mplstyle")
import numpy as np
import h5py
import sys
sys.path.append('./../figures')
from makeCorner import plot_corner
sys.path.append('./../code/')
from utilities import *
```
Load the sumary file containing output of our autoregressive inference on the $\chi$ and $\cos\theta$ distributions of BBHs:
```python
hdata = h5py.File("./ar_chi_cost_summary.hdf","r")
# List attributes
for key in hdata.attrs.keys():
print("{0}: {1}".format(key,hdata.attrs[key]))
# List groups and datasetes
print("\nGroups:")
print(hdata.keys())
print("\nData sets inside ['posterior']:")
print(hdata['posterior'].keys())
```
Created_by: process_chi_cost.py
Downloadable_from: 10.5281/zenodo.8087858
Source_code: https://github.com/tcallister/autoregressive-bbh-inference
Groups:
<KeysViewHDF5 ['posterior']>
Data sets inside ['posterior']:
<KeysViewHDF5 ['R_ref', 'alpha', 'ar_chi_std', 'ar_chi_tau', 'ar_cost_std', 'ar_cost_tau', 'bq', 'chis', 'costs', 'dR_dchis', 'dR_dcosts', 'f_chis', 'f_costs', 'kappa', 'log_dmMax', 'log_dmMin', 'log_f_peak', 'mMax', 'mMin', 'min_log_neff', 'mu_m1', 'nEff_inj_per_event', 'sig_m1']>
The different `hdata['posterior/']` datasets correspond to the following:
| Name | Description |
| :---------- | :---------- |
| `R_ref` | The mean on our AR1 processes over $\chi$ and $\cos\theta$ at $m_1=20\,M_\odot$ and $z=0.2$ |
| `ar_chi_std` | The square of this is the prior variance of our AR process over $\chi$ per autocorrelation length |
| `ar_chi_tau` | The autocorrelation length of our AR process over $\chi$ |
| `ar_cost_std` | The square of this is the prior variance of our AR process over $\cos\theta$ per autocorrelation length |
| `ar_cost_tau` | The autocorrelation length of our AR process over $\cos\theta$ |
| `dR_dchis` | The source-frame volumetric merger $\frac{d\mathcal{R}}{d\ln m_1\,dq\,da_1\,da_2\,d\cos\theta_1\,d\cos\theta_2}$ as a function of spin magnitude, evaluated at $m_1=20\,M_\odot$, $q=1$, $z=0.2$, $\cos\theta_1=\cos\theta_2 = 1$, and $a_1=a_2$, with units $\mathrm{Gpc}^{-3}\mathrm{yr}^{-1}$ |
| `dR_dcosts` | The source-frame volumetric merger $\frac{d\mathcal{R}}{d\ln m_1\,dq\,da_1\,da_2\,d\cos\theta_1\,d\cos\theta_2}$ as a function of cosine spin tilt, evaluated at $m_1=20\,M_\odot$, $q=1$, $z=0.2$, $a_1=a_2 = 0.1$, and $\cos\theta_1=\cos\theta_2$, with units $\mathrm{Gpc}^{-3}\mathrm{yr}^{-1}$ |
| `f_chis` | The logarithm of this quantity is our AR1 process over spin magnitude; the square of this quantity is proportional to `dR_dchis` |
| `f_costs` | The logarithm of this quantity is our AR1 process over cosine spin tilt; the square of this quantity is proportional to `dR_dcosts` |
| `chis` | Set of spin magnitudes over which `dR_dchis` and `f_chis` are defined |
| `costs` | Set of cosine spin tilts over which `dR_dcosts` and `f_costs` are defined |
| `alpha` | Power-law index of the "power law" part our primary mass model |
| `mu_m1` | Mean of the Gaussian peak in our primary mass model |
| `sig_m1` | Standard deviation of the Gaussian peak in our primary mass model |
| `log_f_peak` | Log10 of the fraction of BBHs comprising the Gaussian peak, rather than the power law |
| `mMin` | Mass below which the primary mass distribution goes to zero |
| `mMax` | Mass above which the primary mass distribution goes to zero |
| `log_dmMin` | Log10 of the scale length over which the primary mass distribution is smoothly sent to zero below `mMin` |
| `log_dmMax` | Log10 of the scale length over which the primary mass distribution is smoothly sent to zero above `mMax` |
| `bq` | Power-law index governing the mass ratio distribution |
| `kappa` | This is the power-law index governing growth of the merger rate, assumed to evolve as $(1+z)^\kappa$ |
| `nEff_inj_per_event` | Number of effective injections per event informing our Monte Carlo estimate of detection efficiency |
| `min_log_neff` | For each posterior sample, minimum (log10) number of effective posterior samples informing our Monte Carlo estimates of each event's likelihood, taken over all events |
Make corner plots of all these quantities.
There are a lot of parameters, so split them across a few corner plots.
First, parameters describing the spin AR processes:
```python
plot_data = {
'R_ref':{'data':np.log10(hdata['posterior/R_ref'][()]),'plot_bounds':(-6,4),'label':r'$R_\mathrm{ref}$'},
'chi_std':{'data':hdata['posterior/ar_chi_std'][()],'plot_bounds':(0,4),'label':r'$\sigma_{a}$'},
'chi_tau':{'data':np.log10(hdata['posterior/ar_chi_tau'][()]),'plot_bounds':(-2,2),'label':r'$\log_{10}\tau_{a}$'},
'cost_std':{'data':hdata['posterior/ar_cost_std'][()],'plot_bounds':(0,3),'label':r'$\sigma_{\cos\theta}$'},
'cost_tau':{'data':np.log10(hdata['posterior/ar_cost_tau'][()]),'plot_bounds':(-2,6),'label':r'$\log_{10}\tau_{\cos\theta}$'},
'neff':{'data':hdata['posterior/nEff_inj_per_event'][()],'plot_bounds':(0,20),'label':r'nInj/event'},
'min_neff':{'data':hdata['posterior/min_log_neff'][()],'plot_bounds':(-0.5,2.7),'label':r'Min $\log_{10}$(nEff)'},
}
fig = plt.figure(figsize=(12,12))
plot_corner(fig,plot_data,'#3182bd')
plt.subplots_adjust(hspace=0.1,wspace=0.1)
plt.show()
```
Next, parameters governing the mass, mass ratio, and redshift distributions:
```python
plot_data = {
'kappa':{'data':hdata['posterior/kappa'][()],'plot_bounds':(-2,7),'label':r'$\kappa$'},
'alpha':{'data':hdata['posterior/alpha'][()],'plot_bounds':(-6,-2),'label':r'$\alpha$'},
'mu_m1':{'data':hdata['posterior/mu_m1'][()],'plot_bounds':(25,40),'label':r'$\mu_m$'},
'sig_m1':{'data':hdata['posterior/sig_m1'][()],'plot_bounds':(3,15),'label':r'$\sigma_m$'},
'log_f_peak':{'data':hdata['posterior/log_f_peak'][()],'plot_bounds':(-3,-1.5),'label':r'$\log f_p$'},
'mMin':{'data':hdata['posterior/mMin'][()],'plot_bounds':(5,15),'label':r'$m_\mathrm{min}$'},
'mMax':{'data':hdata['posterior/mMax'][()],'plot_bounds':(50,100),'label':r'$m_\mathrm{max}$'},
'log_dmMin':{'data':hdata['posterior/log_dmMin'][()],'plot_bounds':(-1,1),'label':r'$\log dm_\mathrm{min}$'},
'log_dmMax':{'data':hdata['posterior/log_dmMax'][()],'plot_bounds':(0.5,1.5),'label':r'$\log dm_\mathrm{max}$'},
'bq':{'data':hdata['posterior/bq'][()],'plot_bounds':(-2,6),'label':r'$\beta_q$'},
'neff':{'data':hdata['posterior/nEff_inj_per_event'][()],'plot_bounds':(0,40),'label':r'nInj/event'},
'min_neff':{'data':hdata['posterior/min_log_neff'][()],'plot_bounds':(-0.5,2.7),'label':r'Min $\log_{10}$(nEff)'},
}
fig = plt.figure(figsize=(12,12))
plot_corner(fig,plot_data,'#3182bd')
plt.subplots_adjust(hspace=0.1,wspace=0.1)
plt.show()
```
Let's plot the actual measured distributions of BBH parameters:
### 1. Primary mass
We can show this a few different ways.
First, the below plot shows the source-frame merger rate density $\frac{d\mathcal{R}}{d\ln m_1\,dq\,da_1\,da_2\,d\cos\theta_1\,d\cos\theta_2}$ as a function of $m_1$, evaluated at $q=1$, $z=0.2$, $a_1=a_2=0.1$, and $\cos\theta_1=\cos\theta_2=1$.
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
alpha = hdata['posterior/alpha'][()]
mu_m1 = hdata['posterior/mu_m1'][()]
sig_m1 = hdata['posterior/sig_m1'][()]
log_f_peak = hdata['posterior/log_f_peak'][()]
mMin = hdata['posterior/mMin'][()]
mMax = hdata['posterior/mMax'][()]
log_dmMin = hdata['posterior/log_dmMin'][()]
log_dmMax = hdata['posterior/log_dmMax'][()]
bq = hdata['posterior/bq'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
chis = hdata['posterior/chis'][()]
costs = hdata['posterior/costs'][()]
# Get value of AR processes near chi=0.1 and cost=1
f_chi_01 = f_chis[np.argmin(np.abs(chis-0.1))]
f_cost_1 = f_costs[-1,:]
# Grid over which to evaluate masses
mass_grid = np.linspace(5,100,1000)
dR_dlnm1 = np.zeros((R_ref.size,mass_grid.size))
for i in range(R_ref.size):
# Compute dependence of merger rate on primary mass
# Note that we need to normalize to m1=20, according to our definition of R_ref
f_m1_norm = massModel(20.,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
f_m1 = massModel(mass_grid,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])/f_m1_norm
# Probability density at q=1
p_q_1 = (1.+bq[i])/(1.-(tmp_min/mass_grid)**(1.+bq[i]))
# Combine
# Note that, through the definition of R_ref, this is already defined at z=0.2
# Also note that we need the *squares* of f_chi and f_cost, one per component spin
dR_dlnm1[i,:] = R_ref[i]*f_m1*p_q_1*f_chi_01[i]**2*f_cost_1[i]**2*mass_grid
fig,ax = plt.subplots(figsize=(14,6))
for i in np.random.choice(range(mu_m1.size),500):
ax.plot(mass_grid,dR_dlnm1[i,:],color='#3182bd',alpha=0.25,lw=0.15)
ax.plot(mass_grid,np.median(dR_dlnm1,axis=0),color='black')
ax.plot(mass_grid,np.quantile(dR_dlnm1,0.05,axis=0),color='grey',lw=0.5)
ax.plot(mass_grid,np.quantile(dR_dlnm1,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(8,100)
ax.set_ylim(1e-2,1e4)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xticks([10,30,100])
ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_xlabel('Primary mass [$M_\odot$]',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,\ln m_1^{-1}$]',fontsize=18)
plt.show()
```
We could alternatively show the rate $\frac{d\mathcal{R}}{d\ln m_1\,dq}$ as a function of $m_1$, again evaluated at $q=1$ and $z=0.2$ but now having integrated over spin degrees of freedom; this is most directly comparable to e.g. Fig. 3 of the paper text.
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
alpha = hdata['posterior/alpha'][()]
mu_m1 = hdata['posterior/mu_m1'][()]
sig_m1 = hdata['posterior/sig_m1'][()]
log_f_peak = hdata['posterior/log_f_peak'][()]
mMin = hdata['posterior/mMin'][()]
mMax = hdata['posterior/mMax'][()]
log_dmMin = hdata['posterior/log_dmMin'][()]
log_dmMax = hdata['posterior/log_dmMax'][()]
bq = hdata['posterior/bq'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
chis = hdata['posterior/chis'][()]
costs = hdata['posterior/costs'][()]
# Grid over which to evaluate masses
mass_grid = np.linspace(5,100,1000)
dR_dlnm1 = np.zeros((R_ref.size,mass_grid.size))
for i in range(R_ref.size):
# Compute dependence of merger rate on primary mass
# Note that we need to normalize to m1=20, according to our definition of R_ref
f_m1_norm = massModel(20.,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
f_m1 = massModel(mass_grid,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])/f_m1_norm
# This time, integrate out over the spin magnitude and tilt dimensions
f_chi_integral = np.trapz(f_chis[:,i],chis,axis=0)
f_cost_integral = np.trapz(f_costs[:,i],costs,axis=0)
# Probability density at q=1
p_q_1 = (1.+bq[i])/(1.-(tmp_min/mass_grid)**(1.+bq[i]))
# Combine
# Note that, through the definition of R_ref, this is already defined at z=0.2
# As above, we need the *squared* integrals over chi and cost
dR_dlnm1[i,:] = R_ref[i]*f_m1*p_q_1*f_chi_integral**2*f_cost_integral**2*mass_grid
fig,ax = plt.subplots(figsize=(14,6))
for i in np.random.choice(range(mu_m1.size),500):
ax.plot(mass_grid,dR_dlnm1[i,:],color='#3182bd',alpha=0.25,lw=0.15)
ax.plot(mass_grid,np.median(dR_dlnm1,axis=0),color='black')
ax.plot(mass_grid,np.quantile(dR_dlnm1,0.05,axis=0),color='grey',lw=0.5)
ax.plot(mass_grid,np.quantile(dR_dlnm1,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(8,100)
ax.set_ylim(1e-2,1e3)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xticks([10,30,100])
ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_xlabel('Primary mass [$M_\odot$]',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,\ln m_1^{-1}$]',fontsize=18)
plt.show()
```
Lastly, we could instead plot the normalized probability distribution over $\ln m_1$ (**caution**: note that many papers instead show probability distributions over $m_1$, which will be steeper than that shown here)
```python
# Extract things from the hdf file
alpha = hdata['posterior/alpha'][()]
mu_m1 = hdata['posterior/mu_m1'][()]
sig_m1 = hdata['posterior/sig_m1'][()]
log_f_peak = hdata['posterior/log_f_peak'][()]
mMin = hdata['posterior/mMin'][()]
mMax = hdata['posterior/mMax'][()]
log_dmMin = hdata['posterior/log_dmMin'][()]
log_dmMax = hdata['posterior/log_dmMax'][()]
# Grid over which to evaluate masses
mass_grid = np.linspace(5,100,1000)
p_lnm1 = np.zeros((alpha.size,mass_grid.size))
for i in range(R_ref.size):
# Compute dependence of merger rate on primary mass
p_m1_unnormed = massModel(mass_grid,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
# Normalize and multiply by m1 to obtain p_lnm1
p_lnm1[i,:] = p_m1_unnormed/np.trapz(p_m1_unnormed,mass_grid)*mass_grid
fig,ax = plt.subplots(figsize=(14,6))
for i in np.random.choice(range(mu_m1.size),500):
ax.plot(mass_grid,p_lnm1[i,:],color='#3182bd',alpha=0.25,lw=0.15)
ax.plot(mass_grid,np.median(p_lnm1,axis=0),color='black')
ax.plot(mass_grid,np.quantile(p_lnm1,0.05,axis=0),color='grey',lw=0.5)
ax.plot(mass_grid,np.quantile(p_lnm1,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(8,100)
ax.set_ylim(1e-4,10)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xticks([10,30,100])
ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_xlabel('Primary mass [$M_\odot$]',fontsize=18)
ax.set_ylabel('$p(\ln m_1)$',fontsize=18)
plt.show()
```
### 2. Mass ratio
The below plot shows the source-frame merger rate density $\frac{d\mathcal{R}}{d\ln m_1 dq}$ evaluated at $m_1=20\,M_\odot$ and $z=0.2$ and marginalized over spins.
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
bq = hdata['posterior/bq'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
chis = hdata['posterior/chis'][()]
costs = hdata['posterior/costs'][()]
# Define grid over which to evaluate R(q)
q_grid = np.linspace(1e-3,1,1000)
dR_dqs = np.zeros((R_ref.size,q_grid.size))
for i in range(R_ref.size):
# Probability density over mass ratios at m1=20
m1_ref = 20.
p_qs = (1.+bq[i])*q_grid**bq[i]/(1.-(tmp_min/m1_ref)**(1.+bq[i]))
# Truncate below minimum mass ratio
p_qs[q_grid<tmp_min/m1_ref] = 0
# Integrate over component spins
f_chi_integral = np.trapz(f_chis[:,i],chis,axis=0)
f_cost_integral = np.trapz(f_costs[:,i],costs,axis=0)
# Construct full rate
# Note that, by definition, R_ref already corresponds to the rate at m1=20 Msun and z=0.2
# We still, however, need to multiply by m1 to convert from a rate per mass to a rate per *log* mass
dR_dqs[i,:] = R_ref[i]*p_qs*f_chi_integral**2*f_cost_integral**2*m1_ref
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(dR_dqs.shape[1]),500):
ax.plot(q_grid,dR_dqs[i,:],color='#3182bd',alpha=0.25,lw=0.15,zorder=0)
ax.plot(q_grid,np.median(dR_dqs,axis=0),color='black')
ax.plot(q_grid,np.quantile(dR_dqs,0.05,axis=0),color='grey',lw=0.5)
ax.plot(q_grid,np.quantile(dR_dqs,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1)
ax.set_ylim(0,30)
ax.set_xlabel('Mass ratio',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,q^{-1}$]',fontsize=18)
plt.show()
```
Alternatively, we might integrate over $\ln m_1$ to obtain the merger rate $\frac{d\mathcal{R}}{dq}$ evaluated at $z=0.2$ (and still marginalized over spins).
**Another caution**: when integrating over $m_1$, the remaining overall structure of $R(q)$ is entirely dominated by our assumptions concerning minimum masses and truncations on the $p(q)$ distribution. The very slight jaggedness that can be seen at low $q$ below, for instance, corresponds to locations where $q$ falls below our minimum allowed mass ratio ($2\,M_\odot/m_1$) for different values of $m_1$, and hence $R(q,m_1)$ at these locations is sent to zero. The net effect is to produce an overall $R(q)$ that is sharply decreasing towards smaller $q$, but this is entirely due to our choice to truncate $q$ below some minimum value. Our truncation model here differs slightly from that in e.g. Abbott+ 2023, and so our plot will correspondingly look a bit different from e.g. Fig. 10 in Abbott+.
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
alpha = hdata['posterior/alpha'][()]
mu_m1 = hdata['posterior/mu_m1'][()]
sig_m1 = hdata['posterior/sig_m1'][()]
log_f_peak = hdata['posterior/log_f_peak'][()]
mMin = hdata['posterior/mMin'][()]
mMax = hdata['posterior/mMax'][()]
log_dmMin = hdata['posterior/log_dmMin'][()]
log_dmMax = hdata['posterior/log_dmMax'][()]
bq = hdata['posterior/bq'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
chis = hdata['posterior/chis'][()]
costs = hdata['posterior/costs'][()]
# This time we'll need a 2D grid over primary masses and spins
mass_grid = np.linspace(5.,100.,300)
q_grid = np.linspace(1e-3,1,301)
Ms,Qs = np.meshgrid(mass_grid,q_grid)
dR_dqs = np.zeros((R_ref.size,q_grid.size))
for i in range(R_ref.size):
# Variation in the merger rate over primary masses
f_m1_norm = massModel(20.,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
f_m1 = massModel(Ms,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])/f_m1_norm
# Probability densities over mass ratios, conditioned on each primary mass
p_qs = (1.+bq[i])*Qs**bq[i]/(1.-(tmp_min/Ms)**(1.+bq[i]))
p_qs[Qs<tmp_min/Ms] = 0
# Integrate over component spins
f_chi_integral = np.trapz(f_chis[:,i],chis,axis=0)
f_cost_integral = np.trapz(f_costs[:,i],costs,axis=0)
# Construct full rate over the 2D mass vs. q space
# Note that, by definition, R_ref already corresponds to the rate at m1=20 Msun and z=0.2
dR_dm1s_dqs = R_ref[i]*f_m1*p_qs*f_chi_integral**2*f_cost_integral**2
# Finally, integrate out masses
dR_dqs[i,:] = np.trapz(dR_dm1s_dqs,mass_grid,axis=1)
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(dR_dqs.shape[1]),500):
ax.plot(q_grid,dR_dqs[i,:],color='#3182bd',alpha=0.15,lw=0.15,zorder=0)
ax.plot(q_grid,np.median(dR_dqs,axis=0),color='black')
ax.plot(q_grid,np.quantile(dR_dqs,0.05,axis=0),color='grey',lw=0.5)
ax.plot(q_grid,np.quantile(dR_dqs,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1)
ax.set_ylim(1e-1,1e3)
ax.set_yscale('log')
ax.set_xlabel('Mass ratio',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,q^{-1}$]',fontsize=18)
plt.show()
```
### 3. Component spin magnitudes
First, plot the merger rate $\frac{d\mathcal{R}}{d\ln m_1\,dq\,da_1\,da_2\,d\cos\theta_1\,d\cos\theta_2}$ across the $a_1 = a_2$ line, at fixed $m_1=20\,M_\odot$, $q=1$, $z=0.2$, and $\cos\theta_1=\cos\theta_2=1$.
```python
# Extract things from the hdf file
dR_dchis = hdata['posterior/dR_dchis'][()]
chis = hdata['posterior/chis'][()]
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(dR_dchis.shape[1]),500):
ax.plot(chis,dR_dchis[:,i],color='#3182bd',alpha=0.15,lw=0.15,zorder=0)
ax.plot(chis,np.median(dR_dchis,axis=1),color='black')
ax.plot(chis,np.quantile(dR_dchis,0.05,axis=1),color='grey',lw=0.5)
ax.plot(chis,np.quantile(dR_dchis,0.95,axis=1),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1)
ax.set_ylim(1e-3,1e4)
ax.set_yscale('log')
ax.set_xlabel('Spin Magnitude',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,a^{-2}$]',fontsize=18)
plt.show()
```
Alternatively, plot our results as normalized probability distributions over *individual* component spin magnitudes. This is proportional to the square root of the curves shown above.
```python
# Extract things from the hdf file
f_chis = hdata['posterior/f_chis'][()]
chis = hdata['posterior/chis'][()]
# Construct normalized probability distributions
p_chis = np.zeros(f_chis.shape)
for i in range(f_chis.shape[1]):
p_chis[:,i] = f_chis[:,i]/np.trapz(f_chis[:,i],chis)
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(p_chis.shape[1]),500):
ax.plot(chis,p_chis[:,i],color='#3182bd',alpha=0.15,lw=0.15,zorder=0)
ax.plot(chis,np.median(p_chis,axis=1),color='black')
ax.plot(chis,np.quantile(p_chis,0.05,axis=1),color='grey',lw=0.5)
ax.plot(chis,np.quantile(p_chis,0.95,axis=1),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1)
ax.set_ylim(0,8)
ax.set_xlabel('Spin Magnitude',fontsize=18)
ax.set_ylabel('$p(a)$]',fontsize=18)
plt.show()
```
### 4. Component spin tilts
First, plot the merger rate $\frac{d\mathcal{R}}{d\ln m_1\,dq\,da_1\,da_2\,d\cos\theta_1\,d\cos\theta_2}$ across the $\cos\theta_1 = \cos\theta_2$ line, at fixed $m_1=20\,M_\odot$, $q=1$, $z=0.2$, and $a_1=a_2=0.1$.
```python
# Extract things from the hdf file
dR_dcosts = hdata['posterior/dR_dcosts'][()]
costs = hdata['posterior/costs'][()]
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(dR_dcosts.shape[1]),500):
ax.plot(costs,dR_dcosts[:,i],color='#3182bd',alpha=0.15,lw=0.15,zorder=0)
ax.plot(costs,np.median(dR_dcosts,axis=1),color='black')
ax.plot(costs,np.quantile(dR_dcosts,0.05,axis=1),color='grey',lw=0.5)
ax.plot(costs,np.quantile(dR_dcosts,0.95,axis=1),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(-1,1)
ax.set_ylim(1e-2,1e4)
ax.set_yscale('log')
ax.set_xlabel('Cosine spin tilt',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,\cos\\theta^{-2}$]',fontsize=18)
plt.show()
```
Alternatively, plot our results as normalized probability distributions over *individual* component spin tilts. This is proportional to the square root of the curves shown above.
```python
# Extract things from the hdf file
f_costs = hdata['posterior/f_costs'][()]
costs = hdata['posterior/costs'][()]
# Construct normalized probability distributions
p_costs = np.zeros(f_costs.shape)
for i in range(f_costs.shape[1]):
p_costs[:,i] = f_costs[:,i]/np.trapz(f_costs[:,i],costs)
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(p_costs.shape[1]),500):
ax.plot(costs,p_costs[:,i],color='#3182bd',alpha=0.15,lw=0.15,zorder=0)
ax.plot(costs,np.median(p_costs,axis=1),color='black')
ax.plot(costs,np.quantile(p_costs,0.05,axis=1),color='grey',lw=0.5)
ax.plot(costs,np.quantile(p_costs,0.95,axis=1),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(-1,1)
ax.set_ylim(0,2)
ax.set_xlabel('Cosine spin tilt',fontsize=18)
ax.set_ylabel('$p(\cos\\theta)$',fontsize=18)
plt.show()
```
### 5. Redshifts
Plot the evolution of the merger rate $\frac{d\mathcal{R}}{d\ln m_1 dq}$ across redshift, evaluated at fixed $m_1 = 20\,M_\odot$ and $q=1$.
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
bq = hdata['posterior/bq'][()]
kappa = hdata['posterior/kappa'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
# Grid over which to evaluate R(z)
z_grid = np.linspace(0,1.5,500)
R_zs = np.zeros((R_ref.size,z_grid.size))
for i in range(R_ref.size):
# Integrate over spin magnitudes and tilts
f_chi_integral = np.trapz(f_chis[:,i],chis,axis=0)
f_cost_integral = np.trapz(f_costs[:,i],costs,axis=0)
# Probability density at q=1, given m1=20
p_q_1 = (1.+bq[i])/(1. - (tmp_min/20.)**(1.+bq[i]))
# Construct merger rate at z=0.2, m1=20, q=1
# The first two are already baked into the definition of R_ref,
# although we need to multiply by m1 to convert from dR/dm1 to dR/dlnm1
R_z_02 = R_ref[i]*f_chi_integral**2*f_cost_integral**2*p_q_1*20.
# Now extend across all redshifts according to our power law model
R_zs[i,:] = R_z_02*((1.+z_grid)/(1.+0.2))**kappa[i]
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(R_zs.shape[0]),500):
ax.plot(z_grid,R_zs[i,:],color='#3182bd',alpha=0.25,lw=0.15,zorder=0)
ax.plot(z_grid,np.median(R_zs,axis=0),color='black')
ax.plot(z_grid,np.quantile(R_zs,0.05,axis=0),color='grey',lw=0.5)
ax.plot(z_grid,np.quantile(R_zs,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1.)
ax.set_ylim(1,300)
ax.set_yscale('log')
ax.set_xlabel('Redshift',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}\,\ln m_1^{-1}\,q^{-1}$]',fontsize=18)
plt.show()
```
Instead integrate $\frac{d\mathcal{R}}{d\ln m_1 dq}$ over log-mass and mass ratio, plotting the total inferred merger rate vs. $z$:
```python
# Extract things from the hdf file
R_ref = hdata['posterior/R_ref'][()]
alpha = hdata['posterior/alpha'][()]
mu_m1 = hdata['posterior/mu_m1'][()]
sig_m1 = hdata['posterior/sig_m1'][()]
log_f_peak = hdata['posterior/log_f_peak'][()]
mMin = hdata['posterior/mMin'][()]
mMax = hdata['posterior/mMax'][()]
log_dmMin = hdata['posterior/log_dmMin'][()]
log_dmMax = hdata['posterior/log_dmMax'][()]
bq = hdata['posterior/bq'][()]
kappa = hdata['posterior/kappa'][()]
f_chis = hdata['posterior/f_chis'][()]
f_costs = hdata['posterior/f_costs'][()]
# Grid over which to evaluate R(z)
mass_grid = np.linspace(5,100,1000)
z_grid = np.linspace(0,1.5,500)
R_zs = np.zeros((R_ref.size,z_grid.size))
for i in range(R_ref.size):
# Integrate over spin magnitudes and tilts
f_chi_integral = np.trapz(f_chis[:,i],chis,axis=0)
f_cost_integral = np.trapz(f_costs[:,i],costs,axis=0)
# Compute dependence of merger rate on primary mass
f_m1_norm = massModel(20.,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
f_m1 = massModel(mass_grid,alpha[i],mu_m1[i],sig_m1[i],10.**log_f_peak[i],mMax[i],mMin[i],
10.**log_dmMax[i],10.**log_dmMin[i])
f_m1_integral = np.trapz(f_m1/f_m1_norm,mass_grid)
# Construct full integrated merger rate
R_z_02 = R_ref[i]*f_chi_integral**2*f_cost_integral**2*f_m1_integral
# Now extend across all redshifts according to our power law model
R_zs[i,:] = R_z_02*((1.+z_grid)/(1.+0.2))**kappa[i]
fig,ax = plt.subplots(figsize=(7,6))
for i in np.random.choice(range(R_zs.shape[0]),500):
ax.plot(z_grid,R_zs[i,:],color='#3182bd',alpha=0.25,lw=0.15,zorder=0)
ax.plot(z_grid,np.median(R_zs,axis=0),color='black')
ax.plot(z_grid,np.quantile(R_zs,0.05,axis=0),color='grey',lw=0.5)
ax.plot(z_grid,np.quantile(R_zs,0.95,axis=0),color='grey',lw=0.5)
ax.tick_params(labelsize=18)
ax.set_xlim(0,1.)
ax.set_ylim(3,1000)
ax.set_yscale('log')
ax.set_xlabel('Redshift',fontsize=18)
ax.set_ylabel('Merger Rate [$\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$]',fontsize=18)
plt.show()
```
```python
```
|
tcallisterREPO_NAMEautoregressive-bbh-inferencePATH_START.@autoregressive-bbh-inference_extracted@autoregressive-bbh-inference-main@data@inspect_ar_chi_cost_results.ipynb@.PATH_END.py
|
{
"filename": "use_case_34_fix_blending.py",
"repo_name": "rpoleski/MulensModel",
"repo_path": "MulensModel_extracted/MulensModel-master/examples/use_cases/use_case_34_fix_blending.py",
"type": "Python"
}
|
"""
use_case_34_fix_blending.py
Fix the blending for one observatory to a non-zero value.
This example is modeled after real-time fitting procedures, so the
input datasets are truncated.
For this event, the catalog star in KMT is I > 20. Thus, a fake blending flux
has been added so the baseline is I=20. Thus, for a fixed blending fit, it is
appropriate to fix the blending at the value added to the baseline star.
Adapted from use_case_24_chi2_gradient.py
New functionality marked by # *** NEW ***
"""
import os
import MulensModel
import scipy.optimize as op
class Minimizer(object):
"""
An object to link an Event to the functions necessary to minimize chi2.
"""
def __init__(self, event, parameters_to_fit):
self.event = event
self.parameters_to_fit = parameters_to_fit
def set_parameters(self, theta):
"""for given event set attributes from parameters_to_fit (list of str)
to values from theta list"""
for (key, val) in enumerate(self.parameters_to_fit):
setattr(self.event.model.parameters, val, theta[key])
def chi2_fun(self, theta):
"""for a given set of parameters (theta), return the chi2"""
self.set_parameters(theta)
return self.event.get_chi2()
def chi2_gradient(self, theta):
"""
for a given set of parameters (theta), return the gradient of chi^2
"""
self.set_parameters(theta) # might be redundant, but probably safer
return self.event.get_chi2_gradient(self.parameters_to_fit)
# *** NEW ***
datasets = []
file_names = ['KMTA12_I.pysis', 'KMTC12_I.pysis', 'KMTS12_I.pysis',
'KMTA14_I.pysis', 'KMTC14_I.pysis', 'KMTS14_I.pysis']
data_ref = 1
dir_ = os.path.join(MulensModel.DATA_PATH, "photometry_files", "KB180003")
for i, file_name in enumerate(file_names):
file_ = os.path.join(dir_, file_name)
datasets.append(
MulensModel.MulensData(
file_name=file_, add_2450000=True, usecols=[0, 3, 4],
phot_fmt='mag'))
# Calculate the amount of blending flux that was added.
Icat = 21.89
fcat = MulensModel.Utils.get_flux_from_mag(Icat)
fblend = MulensModel.Utils.get_flux_from_mag(20.) - fcat
# *** END NEW ***
# Fit t_0, t_E for several, fixed values of u_0 with
# fixed blending = added flux
# e.g. to assess whether or not the event could be high-magnification
parameters_to_fit = ["t_0", "t_E"]
for u_0 in [0.0, 0.01, 0.1]:
t_0 = 2457215.
t_E = 20.0
model = MulensModel.Model(
{'t_0': t_0, 'u_0': u_0, 't_E': t_E})
event = MulensModel.Event(datasets=datasets, model=model)
# *** NEW ***
event.fix_blend_flux[datasets[data_ref]] = fblend # Fix the blending =
# the known added value
# *** END NEW ***
initial_guess = [t_0, t_E]
minimizer = Minimizer(event, parameters_to_fit)
result = op.minimize(
minimizer.chi2_fun, x0=initial_guess, method='Newton-CG',
jac=minimizer.chi2_gradient, tol=1e-3)
chi2 = minimizer.chi2_fun(result.x)
print(chi2, u_0, result.x)
print(minimizer.event.fluxes) # *** NEW ***
|
rpoleskiREPO_NAMEMulensModelPATH_START.@MulensModel_extracted@MulensModel-master@examples@use_cases@use_case_34_fix_blending.py@.PATH_END.py
|
{
"filename": "remove_lines.py",
"repo_name": "icecube/toise",
"repo_path": "toise_extracted/toise-main/resources/scripts/2021_midscale/remove_lines.py",
"type": "Python"
}
|
import json
infile = "IceCubeHEX_Sunflower_240m_v3_ExtendedDepthRange.GCD.txt"
file = open(infile, "r")
# load the list of included strings
gsl = open("midscale_geos.json")
options = json.load(gsl)
gsl.close()
gen2_strings = options["corner"] # select the corner
for i, string in enumerate(gen2_strings):
gen2_strings[i] += 1000
strings_to_keep = list(range(1, 87))
strings_to_keep += gen2_strings
# change the name here!
outfile = open("IceCubeHEX_Sunflower_corner_240m_v3_ExtendedDepthRange.GCD.txt", "a")
for i, line in enumerate(file):
pieces = str.split(line)
string = int(pieces[0])
if string in strings_to_keep:
outfile.write(line)
outfile.close()
|
icecubeREPO_NAMEtoisePATH_START.@toise_extracted@toise-main@resources@scripts@2021_midscale@remove_lines.py@.PATH_END.py
|
{
"filename": "serialize_executable.py",
"repo_name": "jax-ml/jax",
"repo_path": "jax_extracted/jax-main/jax/experimental/serialize_executable.py",
"type": "Python"
}
|
# Copyright 2018 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pickling support for precompiled binaries."""
from __future__ import annotations
import pickle
import io
import jax
from jax._src.lib import xla_client as xc
def serialize(compiled: jax.stages.Compiled):
"""Serializes a compiled binary.
Because pytrees are not serializable, they are returned so that
the user can handle them properly.
"""
unloaded_executable = getattr(compiled._executable,
'_unloaded_executable', None)
if unloaded_executable is None:
raise ValueError("Compilation does not support serialization")
args_info_flat, in_tree = jax.tree_util.tree_flatten(compiled.args_info)
with io.BytesIO() as file:
_JaxPjrtPickler(file).dump(
(unloaded_executable, args_info_flat, compiled._no_kwargs))
return file.getvalue(), in_tree, compiled.out_tree
def deserialize_and_load(serialized,
in_tree,
out_tree,
backend: str | xc.Client | None = None):
"""Constructs a jax.stages.Compiled from a serialized executable."""
if backend is None or isinstance(backend, str):
backend = jax.devices(backend)[0].client
(unloaded_executable, args_info_flat,
no_kwargs) = _JaxPjrtUnpickler(io.BytesIO(serialized), backend).load()
args_info = in_tree.unflatten(args_info_flat)
loaded_compiled_obj = unloaded_executable.load()
return jax.stages.Compiled(
loaded_compiled_obj, args_info, out_tree, no_kwargs=no_kwargs)
class _JaxPjrtPickler(pickle.Pickler):
device_types = (xc.Device,)
client_types = (xc.Client,)
def persistent_id(self, obj):
if isinstance(obj, xc.LoadedExecutable):
return ('exec', obj.client.serialize_executable(obj))
if isinstance(obj, xc._xla.Executable):
return ('exec', obj.serialize())
if isinstance(obj, self.device_types):
return ('device', obj.id)
if isinstance(obj, self.client_types):
return ('client',)
class _JaxPjrtUnpickler(pickle.Unpickler):
def __init__(self, file, backend):
super().__init__(file)
self.backend = backend
self.devices_by_id = {d.id: d for d in backend.devices()}
def persistent_load(self, pid):
if pid[0] == 'exec':
return self.backend.deserialize_executable(pid[1])
if pid[0] == 'device':
return self.devices_by_id[pid[1]]
if pid[0] == 'client':
return self.backend
raise pickle.UnpicklingError
|
jax-mlREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@jax@experimental@serialize_executable.py@.PATH_END.py
|
{
"filename": "distributed.py",
"repo_name": "jax-ml/jax",
"repo_path": "jax_extracted/jax-main/jax/_src/distributed.py",
"type": "Python"
}
|
# Copyright 2021 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from collections.abc import Sequence
import logging
import os
from typing import Any
from jax._src import clusters
from jax._src import config
from jax._src import xla_bridge
from jax._src.lib import xla_extension
logger = logging.getLogger(__name__)
_CHECK_PROXY_ENVS = config.bool_flag(
name="jax_check_proxy_envs",
default=True,
help="Checks proxy vars in user envs and emit warnings.",
)
class State:
process_id: int = 0
num_processes: int = 1
service: Any | None = None
client: Any | None = None
preemption_sync_manager: Any | None = None
coordinator_address: str | None = None
def initialize(self,
coordinator_address: str | None = None,
num_processes: int | None = None,
process_id: int | None = None,
local_device_ids: int | Sequence[int] | None = None,
cluster_detection_method: str | None = None,
initialization_timeout: int = 300,
coordinator_bind_address: str | None = None,
service_heartbeat_interval_seconds: int = 10,
service_max_missing_heartbeats: int = 10,
client_heartbeat_interval_seconds: int = 10,
client_max_missing_heartbeats: int = 10):
coordinator_address = (coordinator_address or
os.environ.get('JAX_COORDINATOR_ADDRESS'))
if isinstance(local_device_ids, int):
local_device_ids = [local_device_ids]
if local_device_ids is None and (env_ids := os.environ.get('JAX_LOCAL_DEVICE_IDS')):
local_device_ids = list(map(int, env_ids.split(",")))
if (cluster_detection_method != 'deactivate' and
None in (coordinator_address, num_processes, process_id, local_device_ids)):
(coordinator_address, num_processes, process_id, local_device_ids) = (
clusters.ClusterEnv.auto_detect_unset_distributed_params(
coordinator_address,
num_processes,
process_id,
local_device_ids,
cluster_detection_method,
initialization_timeout,
)
)
if coordinator_address is None:
raise ValueError('coordinator_address should be defined.')
if num_processes is None:
raise ValueError('Number of processes must be defined.')
if process_id is None:
raise ValueError('The process id of the current process must be defined.')
self.coordinator_address = coordinator_address
# The default value of [::]:port tells the coordinator to bind to all
# available addresses on the same port as coordinator_address.
default_coordinator_bind_address = '[::]:' + coordinator_address.rsplit(':', 1)[1]
coordinator_bind_address = (coordinator_bind_address or
os.environ.get('JAX_COORDINATOR_BIND_ADDRESS',
default_coordinator_bind_address))
if coordinator_bind_address is None:
raise ValueError('coordinator_bind_address should be defined.')
if local_device_ids:
visible_devices = ','.join(str(x) for x in local_device_ids)
logger.info('JAX distributed initialized with visible devices: %s', visible_devices)
config.update("jax_cuda_visible_devices", visible_devices)
config.update("jax_rocm_visible_devices", visible_devices)
self.process_id = process_id
proxy_vars = []
if _CHECK_PROXY_ENVS.value:
proxy_vars = [key for key in os.environ.keys()
if '_proxy' in key.lower()]
if len(proxy_vars) > 0:
vars = " ".join(proxy_vars) + ". "
warning = (
f'JAX detected proxy variable(s) in the environment as distributed setup: {vars}'
'On some systems, this may cause a hang of distributed.initialize and '
'you may need to unset these ENV variable(s)'
)
logger.warning(warning)
if process_id == 0:
if self.service is not None:
raise RuntimeError('distributed.initialize should only be called once.')
logger.info(
'Starting JAX distributed service on %s', coordinator_bind_address
)
self.service = xla_extension.get_distributed_runtime_service(
coordinator_bind_address, num_processes,
heartbeat_interval=service_heartbeat_interval_seconds,
max_missing_heartbeats=service_max_missing_heartbeats)
self.num_processes = num_processes
if self.client is not None:
raise RuntimeError('distributed.initialize should only be called once.')
self.client = xla_extension.get_distributed_runtime_client(
coordinator_address, process_id, init_timeout=initialization_timeout,
heartbeat_interval=client_heartbeat_interval_seconds,
max_missing_heartbeats=client_max_missing_heartbeats, use_compression=True)
logger.info('Connecting to JAX distributed service on %s', coordinator_address)
self.client.connect()
self.initialize_preemption_sync_manager()
def shutdown(self):
if self.client:
self.client.shutdown()
self.client = None
if self.service:
self.service.shutdown()
self.service = None
if self.preemption_sync_manager:
self.preemption_sync_manager = None
def initialize_preemption_sync_manager(self):
if self.preemption_sync_manager is not None:
raise RuntimeError(
'Preemption sync manager should only be initialized once.')
self.preemption_sync_manager = (
xla_extension.create_preemption_sync_manager())
self.preemption_sync_manager.initialize(self.client)
global_state = State()
def initialize(coordinator_address: str | None = None,
num_processes: int | None = None,
process_id: int | None = None,
local_device_ids: int | Sequence[int] | None = None,
cluster_detection_method: str | None = None,
initialization_timeout: int = 300,
coordinator_bind_address: str | None = None):
"""Initializes the JAX distributed system.
Calling :func:`~jax.distributed.initialize` prepares JAX for execution on
multi-host GPU and Cloud TPU. :func:`~jax.distributed.initialize` must be
called before performing any JAX computations.
The JAX distributed system serves a number of roles:
* It allows JAX processes to discover each other and share topology information,
* It performs health checking, ensuring that all processes shut down if any process dies, and
* It is used for distributed checkpointing.
If you are using TPU, Slurm, or Open MPI, all arguments are optional: if omitted, they
will be chosen automatically.
The ``cluster_detection_method`` may be used to choose a specific method for detecting those
distributed arguments. You may pass any of the automatic ``spec_detect_methods`` to this
argument though it is not necessary in the TPU, Slurm, or Open MPI cases. For other MPI
installations, if you have a functional ``mpi4py`` installed, you may pass
``cluster_detection_method="mpi4py"`` to bootstrap the required arguments.
Otherwise, you must provide the ``coordinator_address``,
``num_processes``, ``process_id``, and ``local_device_ids`` arguments
to :func:`~jax.distributed.initialize`. When all four arguments are provided, cluster
environment auto detection will be skipped.
Please note: on some systems, particularly HPC clusters that only access external networks
through proxy variables such as HTTP_PROXY, HTTPS_PROXY, etc., the call to
:func:`~jax.distributed.initialize` may timeout. You may need to unset these variables
prior to application launch.
Args:
coordinator_address: the IP address of process `0` and a port on which that
process should launch a coordinator service. The choice of
port does not matter, so long as the port is available on the coordinator
and all processes agree on the port.
May be ``None`` only on supported environments, in which case it will be chosen automatically.
Note that special addresses like ``localhost`` or ``127.0.0.1`` usually mean that the program
will bind to a local interface and are not suitable when running in a multi-host environment.
num_processes: Number of processes. May be ``None`` only on supported environments, in
which case it will be chosen automatically.
process_id: The ID number of the current process. The ``process_id`` values across
the cluster must be a dense range ``0``, ``1``, ..., ``num_processes - 1``.
May be ``None`` only on supported environments; if ``None`` it will be chosen automatically.
local_device_ids: Restricts the visible devices of the current process to ``local_device_ids``.
If ``None``, defaults to all local devices being visible to the process except when processes
are launched via Slurm and Open MPI on GPUs. In that case, it will default to a single device per process.
cluster_detection_method: An optional string to attempt to autodetect the configuration of the distributed
run. Note that "mpi4py" method requires you to have a working ``mpi4py`` install in your environment,
and launch the applicatoin with an MPI-compatible job launcher such as ``mpiexec`` or ``mpirun``.
Legacy auto-detect options "ompi" (OMPI) and "slurm" (Slurm) remain enabled. "deactivate" bypasses
automatic cluster detection.
initialization_timeout: Time period (in seconds) for which connection will
be retried. If the initialization takes more than the timeout specified,
the initialization will error. Defaults to 300 secs i.e. 5 mins.
coordinator_bind_address: the address and port to which the coordinator service
on process `0` should bind. If this is not specified, the default is to bind to
all available addresses on the same port as ``coordinator_address``. On systems
that have multiple network interfaces per node it may be insufficient to only
have the coordinator service listen on one address/interface.
Raises:
RuntimeError: If :func:`~jax.distributed.initialize` is called more than once
or if called after the backend is already initialized.
Examples:
Suppose there are two GPU processes, and process 0 is the designated coordinator
with address ``10.0.0.1:1234``. To initialize the GPU cluster, run the
following commands before anything else.
On process 0:
>>> jax.distributed.initialize(coordinator_address='10.0.0.1:1234', num_processes=2, process_id=0) # doctest: +SKIP
On process 1:
>>> jax.distributed.initialize(coordinator_address='10.0.0.1:1234', num_processes=2, process_id=1) # doctest: +SKIP
"""
if xla_bridge.backends_are_initialized():
raise RuntimeError("jax.distributed.initialize() must be called before "
"any JAX computations are executed.")
global_state.initialize(coordinator_address, num_processes, process_id,
local_device_ids, cluster_detection_method,
initialization_timeout, coordinator_bind_address)
def shutdown():
"""Shuts down the distributed system.
Does nothing if the distributed system is not running.
"""
global_state.shutdown()
|
jax-mlREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@jax@_src@distributed.py@.PATH_END.py
|
{
"filename": "test_gp_priors.py",
"repo_name": "nanograv/enterprise",
"repo_path": "enterprise_extracted/enterprise-master/tests/test_gp_priors.py",
"type": "Python"
}
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_gp_priors
----------------------------------
Tests for GP priors and bases.
"""
import unittest
import numpy as np
from tests.enterprise_test_data import datadir
from enterprise.pulsar import Pulsar
from enterprise.signals import parameter
from enterprise.signals import gp_signals
from enterprise.signals import gp_priors
from enterprise.signals import gp_bases
import scipy.stats
class TestGPSignals(unittest.TestCase):
@classmethod
def setUpClass(cls):
"""Setup the Pulsar object."""
# initialize Pulsar class
cls.psr = Pulsar(datadir + "/B1855+09_NANOGrav_9yv1.gls.par", datadir + "/B1855+09_NANOGrav_9yv1.tim")
def test_turnover_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.turnover(
log10_A=parameter.Uniform(-18, -12),
gamma=parameter.Uniform(1, 7),
lf0=parameter.Uniform(-9, -7.5),
kappa=parameter.Uniform(2.5, 5),
beta=parameter.Uniform(0.01, 1),
)
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
log10_A, gamma, lf0, kappa, beta = -14.5, 4.33, -8.5, 3, 0.5
params = {
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_red_noise_lf0": lf0,
"B1855+09_red_noise_kappa": kappa,
"B1855+09_red_noise_beta": beta,
}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for turnover."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.turnover(f2, log10_A=log10_A, gamma=gamma, lf0=lf0, kappa=kappa, beta=beta)
msg = "Spectrum incorrect for turnover."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for turnover."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_free_spec_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.free_spectrum(log10_rho=parameter.Uniform(-10, -4, size=30))
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
rhos = np.random.uniform(-10, -4, size=30)
params = {"B1855+09_red_noise_log10_rho": rhos}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for free spectrum."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.free_spectrum(f2, log10_rho=rhos)
msg = "Spectrum incorrect for free spectrum."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for free spectrum."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_t_process_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.t_process(
log10_A=parameter.Uniform(-18, -12),
gamma=parameter.Uniform(1, 7),
alphas=gp_priors.InvGamma(alpha=1, gamma=1, size=30),
)
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
alphas = scipy.stats.invgamma.rvs(1, scale=1, size=30)
log10_A, gamma = -15, 4.33
params = {
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_red_noise_alphas": alphas,
}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for free spectrum."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.t_process(f2, log10_A=log10_A, gamma=gamma, alphas=alphas)
msg = "Spectrum incorrect for free spectrum."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for free spectrum."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_adapt_t_process_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.t_process_adapt(
log10_A=parameter.Uniform(-18, -12),
gamma=parameter.Uniform(1, 7),
alphas_adapt=gp_priors.InvGamma(),
nfreq=parameter.Uniform(5, 25),
)
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
alphas = scipy.stats.invgamma.rvs(1, scale=1, size=1)
log10_A, gamma, nfreq = -15, 4.33, 12
params = {
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_red_noise_alphas_adapt": alphas,
"B1855+09_red_noise_nfreq": nfreq,
}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for free spectrum."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.t_process_adapt(f2, log10_A=log10_A, gamma=gamma, alphas_adapt=alphas, nfreq=nfreq)
msg = "Spectrum incorrect for free spectrum."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for free spectrum."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_turnover_knee_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.turnover_knee(
log10_A=parameter.Uniform(-18, -12),
gamma=parameter.Uniform(1, 7),
lfb=parameter.Uniform(-9, -7.5),
lfk=parameter.Uniform(-9, -7.5),
kappa=parameter.Uniform(2.5, 5),
delta=parameter.Uniform(0.01, 1),
)
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
log10_A, gamma, lfb = -14.5, 4.33, -8.5
lfk, kappa, delta = -8.5, 3, 0.5
params = {
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_red_noise_lfb": lfb,
"B1855+09_red_noise_lfk": lfk,
"B1855+09_red_noise_kappa": kappa,
"B1855+09_red_noise_delta": delta,
}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for turnover."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.turnover_knee(f2, log10_A=log10_A, gamma=gamma, lfb=lfb, lfk=lfk, kappa=kappa, delta=delta)
msg = "Spectrum incorrect for turnover."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for turnover."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_broken_powerlaw_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.broken_powerlaw(
log10_A=parameter.Uniform(-18, -12),
gamma=parameter.Uniform(1, 7),
log10_fb=parameter.Uniform(-9, -7.5),
kappa=parameter.Uniform(0.1, 1.0),
delta=parameter.Uniform(0.01, 1),
)
basis = gp_bases.createfourierdesignmatrix_red(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
log10_A, gamma, log10_fb, kappa, delta = -14.5, 4.33, -8.5, 1, 0.5
params = {
"B1855+09_red_noise_log10_A": log10_A,
"B1855+09_red_noise_gamma": gamma,
"B1855+09_red_noise_log10_fb": log10_fb,
"B1855+09_red_noise_kappa": kappa,
"B1855+09_red_noise_delta": delta,
}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_red(self.psr.toas, nmodes=30)
msg = "F matrix incorrect for turnover."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.broken_powerlaw(f2, log10_A=log10_A, gamma=gamma, log10_fb=log10_fb, kappa=kappa, delta=delta)
msg = "Spectrum incorrect for turnover."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for turnover."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
def test_powerlaw_genmodes_prior(self):
"""Test that red noise signal returns correct values."""
# set up signal parameter
pr = gp_priors.powerlaw_genmodes(log10_A=parameter.Uniform(-18, -12), gamma=parameter.Uniform(1, 7))
basis = gp_bases.createfourierdesignmatrix_chromatic(nmodes=30)
rn = gp_signals.BasisGP(priorFunction=pr, basisFunction=basis, name="red_noise")
rnm = rn(self.psr)
# parameters
log10_A, gamma = -14.5, 4.33
params = {"B1855+09_red_noise_log10_A": log10_A, "B1855+09_red_noise_gamma": gamma}
# basis matrix test
F, f2 = gp_bases.createfourierdesignmatrix_chromatic(self.psr.toas, self.psr.freqs, nmodes=30)
msg = "F matrix incorrect for turnover."
assert np.allclose(F, rnm.get_basis(params)), msg
# spectrum test
phi = gp_priors.powerlaw_genmodes(f2, log10_A=log10_A, gamma=gamma)
msg = "Spectrum incorrect for turnover."
assert np.all(rnm.get_phi(params) == phi), msg
# inverse spectrum test
msg = "Spectrum inverse incorrect for turnover."
assert np.all(rnm.get_phiinv(params) == 1 / phi), msg
# test shape
msg = "F matrix shape incorrect"
assert rnm.get_basis(params).shape == F.shape, msg
|
nanogravREPO_NAMEenterprisePATH_START.@enterprise_extracted@enterprise-master@tests@test_gp_priors.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "sibirrer/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/test/test_LensModel/test_Profiles/__init__.py",
"type": "Python"
}
|
sibirrerREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@test@test_LensModel@test_Profiles@__init__.py@.PATH_END.py
|
|
{
"filename": "types_test.py",
"repo_name": "spotify/annoy",
"repo_path": "annoy_extracted/annoy-main/test/types_test.py",
"type": "Python"
}
|
# Copyright (c) 2013 Spotify AB
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
import random
import numpy
import pytest
from annoy import AnnoyIndex
def test_numpy(n_points=1000, n_trees=10):
f = 10
i = AnnoyIndex(f, "euclidean")
for j in range(n_points):
a = numpy.random.normal(size=f)
a = a.astype(
random.choice([numpy.float64, numpy.float32, numpy.uint8, numpy.int16])
)
i.add_item(j, a)
i.build(n_trees)
def test_tuple(n_points=1000, n_trees=10):
f = 10
i = AnnoyIndex(f, "euclidean")
for j in range(n_points):
i.add_item(j, tuple(random.gauss(0, 1) for x in range(f)))
i.build(n_trees)
def test_wrong_length(n_points=1000, n_trees=10):
f = 10
i = AnnoyIndex(f, "euclidean")
i.add_item(0, [random.gauss(0, 1) for x in range(f)])
with pytest.raises(IndexError):
i.add_item(1, [random.gauss(0, 1) for x in range(f + 1000)])
with pytest.raises(IndexError):
i.add_item(2, [])
i.build(n_trees)
def test_range_errors(n_points=1000, n_trees=10):
f = 10
i = AnnoyIndex(f, "euclidean")
for j in range(n_points):
i.add_item(j, [random.gauss(0, 1) for x in range(f)])
with pytest.raises(IndexError):
i.add_item(-1, [random.gauss(0, 1) for x in range(f)])
i.build(n_trees)
for bad_index in [-1000, -1, n_points, n_points + 1000]:
with pytest.raises(IndexError):
i.get_distance(0, bad_index)
with pytest.raises(IndexError):
i.get_nns_by_item(bad_index, 1)
with pytest.raises(IndexError):
i.get_item_vector(bad_index)
def test_missing_len():
"""
We should get a helpful error message if our vector doesn't have a
__len__ method.
"""
class FakeCollection:
pass
i = AnnoyIndex(10, "euclidean")
with pytest.raises(TypeError) as excinfo:
i.add_item(1, FakeCollection())
assert str(excinfo.value) == "object of type 'FakeCollection' has no len()"
def test_missing_getitem():
"""
We should get a helpful error message if our vector doesn't have a
__getitem__ method.
"""
class FakeCollection:
def __len__(self):
return 5
i = AnnoyIndex(5, "euclidean")
with pytest.raises(TypeError) as excinfo:
i.add_item(1, FakeCollection())
assert str(excinfo.value) == "'FakeCollection' object is not subscriptable"
def test_short():
"""
Ensure we handle our vector not being long enough.
"""
class FakeCollection:
def __len__(self):
return 3
def __getitem__(self, i):
raise IndexError
i = AnnoyIndex(3, "euclidean")
with pytest.raises(IndexError):
i.add_item(1, FakeCollection())
def test_non_float():
"""
We should error gracefully if non-floats are provided in our vector.
"""
array_strings = ["1", "2", "3"]
i = AnnoyIndex(3, "euclidean")
with pytest.raises(TypeError) as excinfo:
i.add_item(1, array_strings)
assert str(excinfo.value) == "must be real number, not str"
|
spotifyREPO_NAMEannoyPATH_START.@annoy_extracted@annoy-main@test@types_test.py@.PATH_END.py
|
{
"filename": "reionization.py",
"repo_name": "toshiyan/cmblensplus",
"repo_path": "cmblensplus_extracted/cmblensplus-master/utils/reionization.py",
"type": "Python"
}
|
import numpy as np, tqdm
# from cmblensplus
import basic
# from cmblensplus/utils
import constant as c
# to avoid scipy constants use
from scipy.integrate import quad
from scipy.interpolate import InterpolatedUnivariateSpline as spline
def xHlogxH(xe):
if 1.-xe<1e-6:
return 0.
else:
return (1.-xe)*np.log(1.-xe)
def __nR__(Rc):
if Rc<10.:
return -1.8 + (0.3/9.)*(Rc-10.)
else:
return -1.8 + (1.2/90.)*(Rc-10.)
def Pr(R,Rc,sigma=np.log(2.),evol=False):
if evol:
sigma = np.log(2.) * ( Rc/10 )**(-1.5-0.5*__nR__(Rc))
return 1./(R*np.sqrt(2*np.pi*sigma**2)) * np.exp(-(np.log(R/Rc))**2/(2.*sigma**2))
def __Wth__(x):
return 3./x**3 * (np.sin(x)-x*np.cos(x))
def __Fk__(k,R0,evol=False):
I1 = lambda R: Pr(R,R0,evol=evol)*(4*np.pi*R**3/3.)**2*__Wth__(k*R)**2
I2 = lambda R: Pr(R,R0,evol=evol)*4*np.pi*R**3/3.
neum = quad(I1,0.,2e2)[0]
deno = quad(I2,0.,2e2)[0]
return neum/deno
def __Gk_muint__(k,K,Pk):
I = lambda mu: Pk( np.sqrt(k**2-2*k*K*mu+K**2) )
return quad(I,-1.,1.)[0]
def __Gk__(k,R0,Pk,evol=False):
vFk = np.vectorize(__Fk__)
vGkint = np.vectorize(__Gk_muint__)
Ki = np.logspace(-3,1,11)
dK = Ki[1:]-Ki[:-1]
I = np.sum(dK*Ki[:-1]**2*vGkint(k,Ki[:-1],Pk)/(2*np.pi)**2*vFk(Ki[:-1],R0,evol=evol))
return I
def __Ik__(k,R0,evol=False):
I1 = lambda R: Pr(R,R0,evol=evol) * R**3 * __Wth__(k*R)
I2 = lambda R: Pr(R,R0,evol=evol) * R**3
neum = quad(I1,0.,2e2)[0]
deno = quad(I2,0.,2e2)[0]
return neum/deno
def __cltt__(L,rz,Dz,Hz,Pk,xe,Rz,Kz,bias=6.,zmin=0.,zmax=100.,evol=False):
k = lambda z: L/rz(z)
Pm = lambda z: Dz(z)**2*Pk(k(z))
P1 = lambda z: xe(z) * (1-xe(z)) * ( __Fk__(k(z),Rz(z),evol=evol) + __Gk__(k(z),Rz(z),Pm,evol=evol) )
P2 = lambda z: ( xHlogxH(xe(z)) * bias * __Ik__(k(z),Rz(z),evol=evol) - xe(z) )**2 * Pm(z)
I0 = lambda z: Kz(z)*(P1(z)+P2(z))
return quad(I0,zmin,zmax)[0]
def xe_sym(z,zre=8.,Dz=4.,f_xe=1.08):
y = np.power(1.+z,1.5)
yre = np.power(1.+zre,1.5)
Dy = 1.5*np.sqrt(1.+zre)*Dz
return f_xe*.5*(1.-np.tanh((y-yre)/Dy))
def xe_asym(z,alpha_xe=7.,z_early=20.,zend=6.,f_xe=1.08):
# Planck 2016 (1605.03507) Eq.(3)
if z < zend:
return f_xe
if z >= zend and z<z_early:
return f_xe*np.power((z_early-z)/(z_early-zend),alpha_xe)
if z >= z_early:
return 0.
def compute_cltt(xe,H0=70.,Om=.3,Ov=.7,Ob=.0455,w0=-1.,wa=0.,ns=.97,As=2e-9,R0=10.,alpha=0.,bias=6.,lmin=1,lmax=3000,ln=100,zmin=1e-4,zmax=50,zn=1000,evol=False):
cps = {'H0':H0,'Om':Om,'Ov':Ov,'w0':w0,'wa':wa}
h0 = H0/100.
zi = np.linspace(zmin,zmax,zn)
Hzi = basic.cosmofuncs.hubble(zi,divc=True,**cps)
rzi = basic.cosmofuncs.dist_comoving(zi,**cps)
Dzi = basic.cosmofuncs.growth_factor(zi,normed=True,**cps)
Hz = spline( zi, Hzi )
rz = spline( zi, rzi )
Dz = spline( zi, Dzi )
# evolution of bubble size
if alpha==0.:
Rz = lambda z: R0
else:
Rz = lambda z: R0*np.power(10.,alpha*(xe(z)-.5))
# compute linear matter P(k) at z=0
k, pk0 = cosmology.camb_pk(H0=H0,Om=Om,Ob=Ob,ns=ns,As=As,z=[0.],kmax=20.,minkh=1e-4,maxkh=10,npoints=500)
Pk = spline(k,pk0)
# Kz
Kz = lambda z: (sigmaT*(c.rho_c*Ob*h0**2/c.m_p)*c.Mpc2m)**2 * (1+z)**4/rz(z)**2/Hz(z)
# compute cltt
l = np.linspace(lmin,lmax,ln)
cl = np.zeros(ln)
for i, L in enumerate(tqdm.tqdm(l)):
cl[i] = __cltt__(L,rz,Dz,Hz,Pk,xe,Rz,Kz,bias=bias,evol=evol)
return l, cl
# for tau related
def dtau_dchi(z,xe='tanh',ombh2=0.02254):
if xe=='tanh':
xez = xe_sym(z)
elif xe=='asym':
xez = xe_asym(z)
else: # xe should be a number
xez = xe
#Eq.(3.44) of Dodelson's Modern Cosmology and conversion of H(z) unit 1/Mpc -> 1/m
f = c.sigmaT * (c.rho_c*ombh2/c.m_p) * c.Mpc2m
# dtau/dchi
return f * (1+z)**2 * xez
def optical_depth(xe,H0=70.,Om=.3,Ov=.7,Ob=.0455,w0=-1.,wa=0.,zmin=1e-4,zmax=50,zn=1000):
# precompute H(z)
zi = np.linspace(zmin,zmax,zn)
Hzi = basic.cosmofuncs.hubble(zi,divc=True,H0=H0,Om=Om,Ov=Ov,w0=w0,wa=wa)
Hz = spline( zi, Hzi )
# define z-integral
I = lambda z: dtau_dchi(z,xe(z),ombh2=Ob*(H0*.01)**2)/Hz(z)
# compute z-integral
print('optical depth:', quad(I,zmin,zmax)[0])
|
toshiyanREPO_NAMEcmblensplusPATH_START.@cmblensplus_extracted@cmblensplus-master@utils@reionization.py@.PATH_END.py
|
{
"filename": "mean_weight_system_params.py",
"repo_name": "JamesKirk11/Tiberius",
"repo_path": "Tiberius_extracted/Tiberius-main/src/fitting_utils/mean_weight_system_params.py",
"type": "Python"
}
|
#### Author of this code: James Kirk
#### Contact: jameskirk@live.co.uk
from Tiberius.src.fitting_utils import plotting_utils as pu
import argparse
import numpy as np
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Use this code to give you mean-weighted uncertainties from > 1 white light curve fit')
parser.add_argument('best_fit_tabs',help="the paths to the WL best_fit_parameters.txt tables you want to pull from",nargs='+')
parser.add_argument('-t0_offsets',help="the offsets to be added to the best-fitting t0 values, to convert back into MJD/BJD",nargs='+',type=float)
parser.add_argument('-P','--period',help="the planet's period so that the weighted mean t0 can be computed from multiple visits",type=float)
parser.add_argument('-l','--labels',help="The x-axis labels if wanting to overwrite the default (Table 1, Table 2,...)",nargs="+")
parser.add_argument('-sp_only','--sys_params_only',help="Use this if wanting to plot only the system parameters without systematics parameters. Default is that all parameters are plotted.",action="store_true")
parser.add_argument('-no_mean','--no_mean',help="Use this if wanting to plot only parameters without calculating and plotting the weighted mean",action="store_true")
parser.add_argument('-s','--save_fig',help="Use this if wanting to save the figure",action="store_true")
parser.add_argument('-t','--title',help='use this to define the filename of the saved figure, overwriting the default')
parser.add_argument('-k','--k',help='use this to plot a horizontal line at a desired value of k (Rp/Rs)',type=float)
parser.add_argument('-aRs','--aRs',help='use this to plot a horizontal line at a desired value of aRs (a/Rs)',type=float)
parser.add_argument('-t0','--t0',help='use this to plot a horizontal line at a desired value of t0 (time of mid-transit)',type=float)
parser.add_argument('-inc','--inc',help='use this to plot a horizontal line at a desired value of inc (inclination)',type=float)
args = parser.parse_args()
best_fit_dict = {}
if args.t0_offsets is not None:
norbits = []
for i,t in enumerate(args.best_fit_tabs):
keys,med,up,lo = np.genfromtxt(t,unpack=True,usecols=[0,2,4,6],dtype=str)
nkeys = len(keys)
for j in range(nkeys):
new_key = keys[j].split("_")[0]
value = float(med[j])
value_up = float(up[j])
value_lo = float(lo[j])
if args.sys_params_only:
if new_key not in ["t0","inc","k","u1","u2","aRs","ecc","omega"]:
continue
if i == 0:
best_fit_dict[new_key] = []
best_fit_dict["%s_up"%new_key] = []
best_fit_dict["%s_lo"%new_key] = []
if args.t0_offsets is not None and new_key == "t0":
value += args.t0_offsets[i]
n = np.round((args.t0_offsets[i] - args.t0_offsets[0])/args.period)
norbits.append(int(n))
value -= int(n)*args.period
try:
if value == 0:
value = value_up = value_lo = np.nan
best_fit_dict[new_key].append(value)
best_fit_dict["%s_up"%new_key].append(value_up)
best_fit_dict["%s_lo"%new_key].append(value_lo)
except:
pass
nkeys = len(best_fit_dict.keys())//3
if not args.no_mean:
print("\n***Weighted mean parameters***\n")
weighted_mean_dict = {}
for k in best_fit_dict.keys():
if "up" in k or "lo" in k:
continue
weighted_mean,weighted_mean_error = pu.weighted_mean_uneven_errors(best_fit_dict[k],best_fit_dict["%s_up"%k],best_fit_dict["%s_lo"%k])
weighted_mean_dict[k] = weighted_mean
weighted_mean_dict["%s_err"%k] = weighted_mean_error
print("%s = %f +/- %f"%(k,weighted_mean,weighted_mean_error))
print("\n*******************************\n")
if args.t0_offsets is not None:
for i,t in enumerate(args.t0_offsets):
print("t0, offset %d = %f"%(i+1,weighted_mean_dict["t0"]+norbits[i]*args.period))
ntables = len(args.best_fit_tabs)
# if args.no_mean:
# n_xticks = ntables
# else:
# n_ticks = ntables+1
fig = plt.figure(figsize=(12*(np.ceil(ntables/4)),10))
subplot_counter = 1
for k in best_fit_dict.keys():
if "err" in k or "_lo" in k or "_up" in k or len(best_fit_dict[k]) != ntables:
continue
ax = fig.add_subplot(nkeys//2,2,subplot_counter)
ax.errorbar(np.arange(ntables)+1,best_fit_dict[k],yerr=((best_fit_dict["%s_lo"%k],best_fit_dict["%s_up"%k])),fmt='o',mec='k',capsize=3,lw=2)
if not args.no_mean:
ax.errorbar(ntables+1,weighted_mean_dict[k],yerr=weighted_mean_dict["%s_err"%k],color='r',fmt='o',mec='k',capsize=3,lw=2)
ax.axhline(weighted_mean_dict[k],ls='--',color='k',zorder=0)
if k == "k" and args.k is not None:
ax.axhline(args.k,ls='--',color='r',lw=2,zorder=0)
if k == "aRs" and args.aRs is not None:
ax.axhline(args.aRs,ls='--',color='r',lw=2,zorder=0)
if k == "inc" and args.inc is not None:
ax.axhline(args.inc,ls='--',color='r',lw=2,zorder=0)
if k == "t0" and args.t0 is not None:
ax.axhline(args.t0,ls='--',color='r',lw=2,zorder=0)
ax.set_ylabel("%s"%(k),fontsize=12)
subplot_counter += 1
if args.no_mean:
ax.set_xticks(np.arange(1,ntables+1))
else:
ax.set_xticks(np.arange(1,ntables+2))
if args.labels is None:
if args.no_mean:
ax.set_xticklabels(["Table %s"%(i+1) for i in range(ntables)],fontsize=12)
else:
ax.set_xticklabels(["Table %s"%(i+1) for i in range(ntables)]+["Weighted\nmean"],fontsize=12)
else:
if args.no_mean:
ax.set_xticklabels(["%s"%(i).replace(" ","\n") for i in args.labels],fontsize=12)
else:
ax.set_xticklabels(["%s"%(i).replace(" ","\n") for i in args.labels]+["Weighted\nmean"],fontsize=12)
ax.tick_params(which='minor',bottom=False,top=False,left=True,right=True)#,direction="inout",length=2,width=1.)
if args.save_fig:
if args.title is None:
fig_title = "system_parameters.pdf"
else:
fig_title = args.title
fig.savefig(fig_title,bbox_inches="tight",dpi=260)
plt.show()
|
JamesKirk11REPO_NAMETiberiusPATH_START.@Tiberius_extracted@Tiberius-main@src@fitting_utils@mean_weight_system_params.py@.PATH_END.py
|
{
"filename": "callback_list.py",
"repo_name": "fchollet/keras",
"repo_path": "keras_extracted/keras-master/keras/src/callbacks/callback_list.py",
"type": "Python"
}
|
import concurrent.futures
from keras.src import backend
from keras.src import tree
from keras.src import utils
from keras.src.api_export import keras_export
from keras.src.callbacks.callback import Callback
from keras.src.callbacks.history import History
from keras.src.callbacks.progbar_logger import ProgbarLogger
from keras.src.utils import python_utils
@keras_export("keras.callbacks.CallbackList")
class CallbackList(Callback):
"""Container abstracting a list of callbacks."""
def __init__(
self,
callbacks=None,
add_history=False,
add_progbar=False,
model=None,
**params,
):
"""Container for `Callback` instances.
This object wraps a list of `Callback` instances, making it possible
to call them all at once via a single endpoint
(e.g. `callback_list.on_epoch_end(...)`).
Args:
callbacks: List of `Callback` instances.
add_history: Whether a `History` callback should be added, if one
does not already exist in the `callbacks` list.
add_progbar: Whether a `ProgbarLogger` callback should be added, if
one does not already exist in the `callbacks` list.
model: The `Model` these callbacks are used with.
**params: If provided, parameters will be passed to each `Callback`
via `Callback.set_params`.
"""
self.callbacks = tree.flatten(callbacks) if callbacks else []
self._executor = None
self._async_train = False
self._async_test = False
self._async_predict = False
self._futures = []
self._configure_async_dispatch(callbacks)
self._add_default_callbacks(add_history, add_progbar)
self.set_model(model)
self.set_params(params)
def set_params(self, params):
self.params = params
if params:
for callback in self.callbacks:
callback.set_params(params)
def _configure_async_dispatch(self, callbacks):
# Determine whether callbacks can be dispatched asynchronously.
if not backend.IS_THREAD_SAFE:
return
async_train = True
async_test = True
async_predict = True
if callbacks:
if isinstance(callbacks, (list, tuple)):
for cbk in callbacks:
if getattr(cbk, "async_safe", False):
# Callbacks that expose self.async_safe == True
# will be assumed safe for async dispatch.
continue
if not utils.is_default(cbk.on_batch_end):
async_train = False
if not utils.is_default(cbk.on_train_batch_end):
async_train = False
if not utils.is_default(cbk.on_test_batch_end):
async_test = False
if not utils.is_default(cbk.on_predict_batch_end):
async_predict = False
if async_train or async_test or async_predict:
self._executor = concurrent.futures.ThreadPoolExecutor()
self._async_train = async_train
self._async_test = async_test
self._async_predict = async_predict
def _add_default_callbacks(self, add_history, add_progbar):
"""Adds `Callback`s that are always present."""
self._progbar = None
self._history = None
for cb in self.callbacks:
if isinstance(cb, ProgbarLogger):
self._progbar = cb
elif isinstance(cb, History):
self._history = cb
if self._history is None and add_history:
self._history = History()
self.callbacks.append(self._history)
if self._progbar is None and add_progbar:
self._progbar = ProgbarLogger()
self.callbacks.append(self._progbar)
def set_model(self, model):
if not model:
return
super().set_model(model)
if self._history:
model.history = self._history
for callback in self.callbacks:
callback.set_model(model)
def _async_dispatch(self, fn, *args):
for future in self._futures:
if future.done():
future.result()
self._futures.remove(future)
future = self._executor.submit(fn, *args)
self._futures.append(future)
def _clear_futures(self):
for future in self._futures:
future.result()
self._futures = []
def on_batch_begin(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_batch_begin(batch, logs=logs)
def on_epoch_begin(self, epoch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_epoch_begin(epoch, logs)
def on_epoch_end(self, epoch, logs=None):
if self._async_train:
self._clear_futures()
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_epoch_end(epoch, logs)
def on_train_batch_begin(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_train_batch_begin(batch, logs=logs)
def on_test_batch_begin(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_test_batch_begin(batch, logs=logs)
def on_predict_batch_begin(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_predict_batch_begin(batch, logs=logs)
def on_batch_end(self, batch, logs=None):
if self._async_train:
self._async_dispatch(self._on_batch_end, batch, logs)
else:
self._on_batch_end(batch, logs)
def on_train_batch_end(self, batch, logs=None):
if self._async_train:
self._async_dispatch(self._on_train_batch_end, batch, logs)
else:
self._on_train_batch_end(batch, logs)
def on_test_batch_end(self, batch, logs=None):
if self._async_test:
self._async_dispatch(self._on_test_batch_end, batch, logs)
else:
self._on_test_batch_end(batch, logs)
def on_predict_batch_end(self, batch, logs=None):
if self._async_predict:
self._async_dispatch(self._on_predict_batch_end, batch, logs)
else:
self._on_predict_batch_end(batch, logs)
def _on_batch_end(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_batch_end(batch, logs=logs)
def _on_train_batch_end(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_train_batch_end(batch, logs=logs)
def _on_test_batch_end(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_test_batch_end(batch, logs=logs)
def _on_predict_batch_end(self, batch, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_predict_batch_end(batch, logs=logs)
def on_train_begin(self, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_train_begin(logs)
def on_train_end(self, logs=None):
if self._async_train:
self._clear_futures()
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_train_end(logs)
def on_test_begin(self, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_test_begin(logs)
def on_test_end(self, logs=None):
if self._async_test:
self._clear_futures()
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_test_end(logs)
def on_predict_begin(self, logs=None):
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_predict_begin(logs)
def on_predict_end(self, logs=None):
if self._async_predict:
self._clear_futures()
logs = python_utils.pythonify_logs(logs)
for callback in self.callbacks:
callback.on_predict_end(logs)
|
fcholletREPO_NAMEkerasPATH_START.@keras_extracted@keras-master@keras@src@callbacks@callback_list.py@.PATH_END.py
|
{
"filename": "TestKernels.py",
"repo_name": "dokester/BayesicFitting",
"repo_path": "BayesicFitting_extracted/BayesicFitting-master/BayesicFitting/test/TestKernels.py",
"type": "Python"
}
|
# run with : python3 -m unittest TestKernels
import unittest
import os
import numpy as numpy
from astropy import units
import matplotlib.pyplot as plt
import warnings
from BayesicFitting import *
from BayesicFitting import formatter as fmt
__author__ = "Do Kester"
__year__ = 2017
__license__ = "GPL3"
__version__ = "0.9"
__maintainer__ = "Do"
__status__ = "Development"
# * This file is part of the BayesicFitting package.
# *
# * BayesicFitting is free software: you can redistribute it and/or modify
# * it under the terms of the GNU Lesser General Public License as
# * published by the Free Software Foundation, either version 3 of
# * the License, or ( at your option ) any later version.
# *
# * BayesicFitting is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# * GNU Lesser General Public License for more details.
# *
# * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
# *
# * 2006 Do Kester
class TestKernels( unittest.TestCase ):
"""
Test harness for Models
Author: Do Kester
"""
def __init__( self, testname ):
super( ).__init__( testname )
self.doplot = ( "DOPLOT" in os.environ and os.environ["DOPLOT"] == "1" )
def testHuber( self ) :
kernel = Huber()
xhm = kernel.fwhm / 2
self.assertAlmostEqual( kernel.result( xhm ), 0.5 )
self.assertAlmostEqual( kernel.result( -xhm ), 0.5 )
if self.doplot :
self.plotK( [Huber] )
def testGauss( self ) :
print( "**** Gauss ********************" )
km = KernelModel( kernel=Gauss() )
gm = GaussModel()
k2 = Kernel2dModel( kernel=Gauss() )
x = numpy.asarray( [-1.0, -0.4, 0.0, 0.4, 1.0] )
x += 0.02
p = [1.0, 0.0, 0.2]
x2 = numpy.append( x, x ).reshape( 2, -1 ).T
p2 = [1.0, 0.0, 0.0, 0.2]
print( fmt( x2 ) )
print( fmt( gm.result( x, p ) ) )
print( fmt( km.result( x, p ) ) )
print( fmt( k2.result( x2, p2 ) ) )
print( fmt( gm.partial( x, p ) ) )
print( fmt( km.partial( x, p ) ) )
print( fmt( k2.partial( x2, p2 ) ) )
print( fmt( gm.derivative( x, p ) ) )
print( fmt( km.derivative( x, p ) ) )
print( fmt( k2.derivative( x2, p2 ) ) )
x3 = numpy.asarray( [[-1.0, -0.8], [-0.6, -0.4], [-0.2, 0.0], [0.2, 0.4], [0.6, 0.8],
[1.0, -1.0], [-0.8, -0.6], [-0.4, -0.2], [0.0, 0.2], [0.4, 0.6], [0.8, 1.0]] )
p3 = [-1.1, 0.5, 0.04, 1.2]
print( "k2 df ", fmt( k2.derivative( x3[0:1,:], p3 ), max=None ) )
print( "k2 num ", fmt( k2.strictNumericDerivative( x3[0:1,:], p3 ), max=None ) )
print( fmt( gm.derivative( x3[0:1,0], [-1.1, 0.5, 1.2] ) ) )
print( fmt( gm.strictNumericDerivative( x3[0:1,0], [-1.1, 0.5, 1.2] ) ) )
print( fmt( gm.derivative( x3[0:1,1], [-1.1, 0.04, 1.2] ) ) )
print( fmt( gm.strictNumericDerivative( x3[0:1,1], [-1.1, 0.04, 1.2] ) ) )
def testKernels( self ):
kernels = [Gauss, Lorentz, Sinc, Biweight, Cosine, CosSquare, Parabola, Triangle,
Tricube, Triweight, Uniform]
if self.doplot :
self.plotK( kernels )
for kernl in kernels :
kernel = kernl()
self.stdKerneltest( kernel, plot=self.doplot )
self.assertTrue( kernel.isBound() or ( kernl in kernels[:3] ) )
def testTHC( self ) :
x = numpy.linspace( -5, 5, 1001 )
for kc in range( 7 ) :
kernel = Tophat( nconv=kc )
print( "****" , kernel, " *************************" )
y = kernel.result( x )
top = y[500]
kernin = numpy.sum( y ) / 100
self.assertAlmostEqual( kernin, kernel.integral )
print( kc, top, numpy.sum( y )/100 )
if self.doplot :
plt.plot( x, kernel.result( x ) )
plt.plot( x, kernel.partial( x ) )
self.stdKerneltest( kernel )
if self.doplot :
plt.show()
def testonex( self ) :
sc = Sinc()
ysc = sc.result( 0.0 )
print( "sinc ", ysc, ysc.__class__ )
un = Uniform()
yun = un.result( 0.0 )
print( "unif ", yun, yun.__class__ )
for n in range( 7 ) :
thc = Tophat( nconv=n )
ytc = thc.result( 0.0 )
print( "thc%d " % n, ytc, ytc.__class__ )
def stdKerneltest( self, kernel, plot=None ):
x = numpy.asarray( [-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0] )
x += 0.02
model = KernelModel( kernel=kernel )
print( "********************************************" )
print( model )
numpy.set_printoptions( precision=3, suppress=False )
par = [1.0,0.0,1.0]
print( model.partial( x, par ) )
model.testPartial( x[5], model.parameters )
model.testPartial( x[0], par )
model.xUnit = units.m
model.yUnit = units.kg
for k in range( model.getNumberOfParameters() ):
print( "%d %-12s %-12s"%(k, model.getParameterName( k ), model.getParameterUnit( k ) ) )
xx = numpy.linspace( 0, 101, 101000 )
yy = model.result( xx )
sn = numpy.sum( yy ) / 500
ss = kernel.integral
print( "Integral ", sn, model.getIntegralUnit( ) )
self.assertAlmostEqual( sn, 1.0, 1 )
self.assertAlmostEqual( ss, 1.0 / model.parameters[0], 4 )
xhm = kernel.fwhm / 2
yhm = model.result( xhm )[0]
self.assertAlmostEqual( yhm, model.result( -xhm ) )
y0 = model.result( 0.0 )[0]
print( xhm, yhm, y0, yhm / y0, yhm.__class__ )
self.assertAlmostEqual( (yhm / y0), 0.5 )
part = model.partial( x, par )
nump = model.numPartial( x, par )
print( part.shape, nump.shape )
for k in range( 11 ) :
print( "%3d %8.3f"%(k, x[k]), part[k,:], nump[k,:] )
k = 0
np = model.npbase
for (pp,nn) in zip( part.flatten(), nump.flatten() ) :
print( "%3d %10.4f %10.4f %10.4f"%(k, x[k//np], pp, nn) )
self.assertAlmostEqual( pp, nn, 3 )
k += 1
mc = model.copy( )
for k in range( mc.getNumberOfParameters() ):
print( "%d %-12s %-12s"%(k, mc.getParameterName( k ), mc.getParameterUnit( k ) ) )
mc.parameters = par
model.parameters = par
for (k,xk,r1,r2) in zip( range( x.size ), x, model.result( x ), mc.result( x ) ) :
print( "%3d %8.3f %10.3f %10.3f" % ( k, xk, r1, r2 ) )
self.assertEqual( r1, r2 )
def plotK( self, kernels ) :
x = numpy.linspace( -5, +5, 1001 )
par = [1.0,0.0,1.0]
for kernel in kernels :
model = kernel()
print( model )
y = model.result( x )
plt.plot( x, y, '-', linewidth=2 )
x2 = numpy.linspace( -4, +4, 11 )
dy = model.partial( x2 )
print( dy )
yy = model.result( x2 )
print( yy )
for k in range( 11 ) :
x3 = numpy.asarray( [-0.05, +0.05] )
y3 = x3 * dy[k] + yy[k]
plt.plot( x2[k] + x3, y3, 'r-' )
plt.show()
@classmethod
def suite( cls ):
return unittest.TestCase.suite( TestModels.__class__ )
if __name__ == '__main__':
unittest.main( )
|
dokesterREPO_NAMEBayesicFittingPATH_START.@BayesicFitting_extracted@BayesicFitting-master@BayesicFitting@test@TestKernels.py@.PATH_END.py
|
{
"filename": "detector.py",
"repo_name": "lucabaldini/ixpeobssim",
"repo_path": "ixpeobssim_extracted/ixpeobssim-main/ixpeobssim/binning/detector.py",
"type": "Python"
}
|
# Copyright (C) 2015--2022, the ixpeobssim team.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""Binning data products in detector coordinates.
"""
from __future__ import print_function, division
from astropy.io import fits
import numpy
from ixpeobssim.binning.base import xEventBinningBase, xBinnedFileBase
from ixpeobssim.core.hist import xGpdMap2d
from ixpeobssim.instrument.gpd import gpd_map_binning, GPD_PHYSICAL_HALF_SIDE_X,\
GPD_PHYSICAL_HALF_SIDE_Y
from ixpeobssim.utils.logging_ import logger
# pylint: disable=invalid-name, attribute-defined-outside-init, too-few-public-methods
# pylint: disable=no-member, arguments-differ
class xEventBinningARMAP(xEventBinningBase):
"""Class for ARMAP binning.
"""
INTENT = 'rate per unit area map in detector coordinates'
SUPPORTED_KWARGS = ['npix']
def process_data(self):
"""Convenience function factoring out the code in common with the
corresponding EFLUX class---see the overloaded method in there.
Here we are binning the event position in detector coordinates
and dividing by the livetime and the bin area. The function returns
a n x n array of area rate values to be written in the output file.
"""
detx, dety = self.event_file.det_position_data()
xbinning, ybinning = gpd_map_binning(GPD_PHYSICAL_HALF_SIDE_X,
GPD_PHYSICAL_HALF_SIDE_Y, self.get('npix'))
bin_area = (xbinning[1] - xbinning[0]) * (ybinning[1] - ybinning[0])
rate, _, _ = numpy.histogram2d(detx, dety, bins=(xbinning, ybinning))
rate /= self.event_file.livetime() * bin_area
return rate
def bin_(self):
"""Overloaded method.
"""
rate = self.process_data()
primary_hdu = self.build_primary_hdu(rate)
primary_hdu.add_keyword('TOTCNTS', self.event_file.num_events(),
'total counts in the original photon list')
hdu_list = fits.HDUList([primary_hdu])
self.write_output_file(hdu_list)
class xBinnedAreaRateMap(xBinnedFileBase):
"""Display interface to binned ARMAP files.
"""
Z_TITLE = 'Dead-time corrected rate [Hz mm$^{-2}$]'
def _read_data(self):
"""Overloaded method.
"""
self.data = self.hdu_list['PRIMARY'].data.T
self.counts = self.hdu_list['PRIMARY'].header['TOTCNTS']
def __iadd__(self, other):
"""Overloaded method for ARMAP binned data addition.
Note that, given the peculiarity of this binned data product
(we are typically interested in the area rate per detector)
we are doing a weighted average of the input data based on the
number of counts for each DU in the original event list.
"""
self._check_iadd(other)
self.data = (self.data * self.counts + other.data * other.counts) /\
(self.counts + other.counts)
self.counts += other.counts
return self
def plot(self):
"""Plot the data.
"""
threshold = 0.1
sel = self.data[self.data > threshold * self.data.max()]
logger.info('Average = %.3f (threshold = %.3f), maximum = %.3f',
sel.mean(), threshold, sel.max())
npix = self.data.shape[0]
hist = xGpdMap2d(npix, zlabel=self.Z_TITLE)
hist.set_content(self.data, self.data / self.data.sum() * self.counts)
hist.plot()
class xEventBinningEFLUX(xEventBinningARMAP):
"""Class for EFLUX binning.
"""
INTENT = 'energy-flux map in detector coordinates'
def process_data(self):
"""Overloaded method.
Here we are just multiplying by the average measured event energy.
"""
rate = xEventBinningARMAP.process_data(self)
rate *= self.event_file.energy_data(mc=False).mean()
return rate
class xBinnedAreaEnergyFluxMap(xBinnedAreaRateMap):
"""Display interface to binned EFLUX files.
"""
Z_TITLE = 'Dead-time corrected energy flux [keV mm$^{-2}$ s$^{-1}$]'
|
lucabaldiniREPO_NAMEixpeobssimPATH_START.@ixpeobssim_extracted@ixpeobssim-main@ixpeobssim@binning@detector.py@.PATH_END.py
|
{
"filename": "weighted_quantiles.py",
"repo_name": "TRASAL/frbpoppy",
"repo_path": "frbpoppy_extracted/frbpoppy-master/tests/monte_carlo/weighted_quantiles.py",
"type": "Python"
}
|
import numpy as np
def quantile_1D(data, weights, quantile):
"""
Compute the weighted quantile of a 1D numpy array.
Parameters
----------
data : ndarray
Input array (one dimension).
weights : ndarray
Array with the weights of the same size of `data`.
quantile : float
Quantile to compute. It must have a value between 0 and 1.
Returns
-------
quantile_1D : float
The output value.
"""
# Check the data
if not isinstance(data, np.matrix):
data = np.asarray(data)
if not isinstance(weights, np.matrix):
weights = np.asarray(weights)
nd = data.ndim
if nd != 1:
raise TypeError("data must be a one dimensional array")
ndw = weights.ndim
if ndw != 1:
raise TypeError("weights must be a one dimensional array")
if data.shape != weights.shape:
raise TypeError("the length of data and weights must be the same")
if ((quantile > 1.) or (quantile < 0.)):
raise ValueError("quantile must have a value between 0. and 1.")
# Sort the data
ind_sorted = np.argsort(data)
sorted_data = data[ind_sorted]
sorted_weights = weights[ind_sorted]
# Compute the auxiliary arrays
Sn = np.cumsum(sorted_weights)
# TODO: Check that the weights do not sum zero
# assert Sn != 0, "The sum of the weights must not be zero"
Pn = (Sn-0.5*sorted_weights)/Sn[-1]
# Get the value of the weighted median
return np.interp(quantile, Pn, sorted_data)
def quantile(data, weights, quantile):
"""
Weighted quantile of an array with respect to the last axis.
Parameters
----------
data : ndarray
Input array.
weights : ndarray
Array with the weights. It must have the same size of the last
axis of `data`.
quantile : float
Quantile to compute. It must have a value between 0 and 1.
Returns
-------
quantile : float
The output value.
"""
# TODO: Allow to specify the axis
nd = data.ndim
if nd == 0:
TypeError("data must have at least one dimension")
elif nd == 1:
return quantile_1D(data, weights, quantile)
elif nd > 1:
n = data.shape
imr = data.reshape((np.prod(n[:-1]), n[-1]))
result = np.apply_along_axis(quantile_1D, -1, imr, weights, quantile)
return result.reshape(n[:-1])
def median(data, weights):
"""
Weighted median of an array with respect to the last axis.
Alias for `quantile(data, weights, 0.5)`.
"""
return quantile(data, weights, 0.5)
|
TRASALREPO_NAMEfrbpoppyPATH_START.@frbpoppy_extracted@frbpoppy-master@tests@monte_carlo@weighted_quantiles.py@.PATH_END.py
|
{
"filename": "helpers.py",
"repo_name": "radio-astro-tools/spectral-cube",
"repo_path": "spectral-cube_extracted/spectral-cube-master/spectral_cube/tests/helpers.py",
"type": "Python"
}
|
from astropy import units as u
from numpy.testing import assert_allclose as assert_allclose_numpy, assert_array_equal
def assert_allclose(q1, q2, **kwargs):
"""
Quantity-safe version of Numpy's assert_allclose
"""
if isinstance(q1, u.Quantity) and isinstance(q2, u.Quantity):
assert_allclose_numpy(q1.to(q2.unit).value, q2.value, **kwargs)
elif isinstance(q1, u.Quantity):
assert_allclose_numpy(q1.value, q2, **kwargs)
elif isinstance(q2, u.Quantity):
assert_allclose_numpy(q1, q2.value, **kwargs)
else:
assert_allclose_numpy(q1, q2, **kwargs)
|
radio-astro-toolsREPO_NAMEspectral-cubePATH_START.@spectral-cube_extracted@spectral-cube-master@spectral_cube@tests@helpers.py@.PATH_END.py
|
{
"filename": "create_GCK_visits_table.py",
"repo_name": "rbuehler/vasca",
"repo_path": "vasca_extracted/vasca-main/vasca/examples/GALEX_DS_GCK/create_GCK_visits_table.py",
"type": "Python"
}
|
"""
Create a complete list of all GALEX CAUSE Kepler survey visits.
This script reads all mcat files relative to the specified root data directory and
extracts relevant information from the FITS header. A combined list for all mcat files
is created. All required information is contained that VASCA field and visits tables can
be created.
"""
import os
import numpy as np
import pandas as pd
from astropy.io import fits
from astropy.table import Table
from astropy.time import Time
import vasca.resource_manager as vascarm
import vasca.utils as vutils
def get_hdr_info(hdr):
"""
Compile data from an mcat file header for general information about
a drift scan observation.
"""
# Extract header info
hdr_info = {
k: hdr[k]
for k in [
"TILENUM",
"TILENAME",
"OBJECT",
"VISIT",
"SUBVIS",
"OBSDATIM",
"NEXPSTAR",
"NEXPTIME",
"RA_CENT",
"DEC_CENT",
"GLONO",
"GLATO",
]
}
# Create names and IDs for fields and visits
field_name = (
f'{hdr_info["TILENUM"]}-{hdr_info["TILENAME"]}_sv{hdr_info["SUBVIS"]:02}'
)
field_id = vutils.name2id(field_name, bits=64)
vis_name = f'{field_name}_{hdr_info["VISIT"]:04}-img'
vis_id = vutils.name2id(vis_name, bits=64)
hdr_info.update(
{
"field_name": field_name,
"field_id": field_id,
"vis_name": vis_name,
"vis_id": vis_id,
}
)
# Time stamp
time_bin_start = Time(hdr_info["NEXPSTAR"], format="unix").mjd
hdr_info["time_bin_start"] = time_bin_start
# Other info
hdr_info.update(
{
"observatory": "GALEX_DS", # GALEX drift scan
"obs_filter": "NUV",
"fov_diam": -999.99, # FoV undefined for drift scan
"sel": 0,
}
)
return hdr_info
# Settings
# Input/output directories
with vascarm.ResourceManager() as rm:
root_data_dir = rm.get_path("gal_ds_fields", "lustre")
out_dir = (os.sep).join(
rm.get_path("gal_ds_visits_list", "lustre").split(os.sep)[:-1]
)
# Load visual image quality table
df_img_quality = pd.read_csv(
f"{out_dir}/GALEX_DS_GCK_visits_img_quality.csv", index_col=0
)
# Load visual image quality table
df_img_quality = pd.read_csv(
f"{out_dir}/GALEX_DS_GCK_visits_img_quality.csv", index_col=0
)
# List of visits with bad image quality
visit_is_bad = df_img_quality.query("quality in ['bad']").vis_name.tolist()
# Dry-run, don't export final list
dry_run = False
# Loops over mcat files and saves info
info = list()
for path, subdirs, files in os.walk(root_data_dir):
for name in files:
# Gets visit name
if name.endswith("-xd-mcat.fits"):
vis_name = os.path.join(path, name).split(os.sep)[-2]
else:
vis_name = None
# Select mcat file for visits if not bad image quality
if name.endswith("-xd-mcat.fits") and vis_name not in visit_is_bad:
# Load mcat file and get relevant info
mcat_path = os.path.join(path, name)
with fits.open(mcat_path) as hdul:
hdr_info = get_hdr_info(hdul[0].header)
# Cross-checks
# File name matches 'OBJECT' key
if (
mcat_path.split(os.sep)[-1].rstrip("-xd-mcat.fits")
!= hdr_info["OBJECT"]
):
print(
"Warning: OBJECT key inconsistent "
f'(OBJECT: {hdr_info["OBJECT"]}, '
f'mcat: {mcat_path.split(os.sep)[-1].rstrip("-xd-mcat.fits")})'
)
# Visit directory name matches 'vis_name' key
if mcat_path.split(os.sep)[-2] != hdr_info["vis_name"]:
print(
"Warning: vis_name key inconsistent: "
f'(vis_name: {hdr_info["vis_name"]}, '
f"mcat directory: {mcat_path.split(os.sep)[-2]})"
)
info.append(hdr_info)
# Combines to astropy table via DataFrame
# because of problematic dtype handling of IDs
# tt_info = Table(info) # this fails second cross-check
df_info = pd.DataFrame(info)
tt_info = Table.from_pandas(df_info)
# Cross-checks
# All visit IDs are unique
vis_ids = np.unique(tt_info["vis_id"])
if not len(vis_ids) == len(tt_info):
raise ValueError("Non-unique visit IDs")
# All visit IDs have been consistently created from visit name
if not all(
[
int(vis_id) == vutils.name2id(vis_name, bits=64)
for vis_id, vis_name in zip(tt_info["vis_id"], tt_info["vis_name"])
]
):
raise ValueError("Inconsistent mapping vis_name to vis_id.")
if not dry_run:
# Export
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
# FITS
tt_info.write(f"{out_dir}/GALEX_DS_GCK_visits_list.fits", overwrite=True)
# CSV
df_info.to_csv(f"{out_dir}/GALEX_DS_GCK_visits_list.csv")
# HTML
df_info.to_html(f"{out_dir}/GALEX_DS_GCK_visits_list.html")
# Loads image quality table
visits_list_dir = (os.sep).join(
rm.get_path("gal_ds_visits_list", "lustre").split(os.sep)[:-1]
)
df_img_quality = pd.read_csv(
f"{visits_list_dir}/GALEX_DS_GCK_visits_img_quality.csv", index_col=0
)
# Passes if not a single bad visit is included in verify table
assert all(
[
name not in tt_info["vis_name"]
for name in df_img_quality.query("quality == 'bad'").vis_name
]
)
|
rbuehlerREPO_NAMEvascaPATH_START.@vasca_extracted@vasca-main@vasca@examples@GALEX_DS_GCK@create_GCK_visits_table.py@.PATH_END.py
|
{
"filename": "wendland.ipynb",
"repo_name": "j0r1/GRALE2",
"repo_path": "GRALE2_extracted/GRALE2-master/pygrale/doc/source/_static/wendland.ipynb",
"type": "Jupyter Notebook"
}
|
This notebook illustrates the [LensPerfect](http://adsabs.harvard.edu/abs/2008ApJ...681..814C) method
```python
%matplotlib inline
import grale.lenses as lenses
import grale.cosmology as cosmology
import grale.plotutil as plotutil
import grale.feedback as feedback
import grale.images as images
from grale.constants import *
import numpy as np
import matplotlib.pyplot as plt
cosm = cosmology.Cosmology(0.7, 0.27, 0, 0.73)
V = lambda x,y: np.array([x,y], dtype=np.double)
LI = plotutil.LensInfo
feedback.setDefaultFeedback("notebook")
plotutil.setDefaultAngularUnit(ANGLE_ARCSEC)
```
```python
# Let's load the real lens, and plot it
realLens = lenses.GravitationalLens.load("reallens_nosheet.lensdata")
size = 220*ANGLE_ARCSEC
lensInfo = LI(realLens, size=size, Ds=1, Dds=1)
plotutil.plotDensityInteractive(lensInfo);
```
<p>Failed to display Jupyter Widget of type <code>Text</code>.</p>
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If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean
that the widgets JavaScript is still loading. If this message persists, it
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</p>
<iframe srcdoc='
<html>
<head>
<link href="https://cdnjs.cloudflare.com/ajax/libs/vis/4.21.0/vis.min.css" type="text/css" rel="stylesheet" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.21.0/vis.min.js" type="text/javascript"></script>
<script src="https://cdn.rawgit.com/gliffy/canvas2svg/master/canvas2svg.js" type="text/javascript"></script>
<script src="https://cdn.rawgit.com/eligrey/FileSaver.js/b4a918669accb81f184c610d741a4a8e1306aa27/FileSaver.js" type="text/javascript"></script>
</head>
<body>
<div id="pos" style="top:0px;left:0px;position:absolute;"></div>
<div id="visualization"></div>
<script type="text/javascript">
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```python
# The lens' angular diameter distance is 0.4 in this cosmology, as is illustrated by
# letting an algorithm look for the match
Dd = realLens.getLensDistance()
zd = min(cosm.findRedshiftForAngularDiameterDistance(Dd))
zd
```
0.40000056290430064
```python
# We'll plot the critical curves and caustics. Not only do they look nice,
# but this also provides us with a LensPlane instance that we'll use below
plt.figure(figsize=(8,8))
plotutil.plotImagePlane(lensInfo);
```
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```python
# Here we obtain the 'LensPlane' for this lens, which contains a map of
# deflection angles at a large number of grid points. We'll be able to
# use this to trace a point in the source plane to its corresponding
# image locations
lensplane = lensInfo.getLensPlane()
```
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```python
# We're going to create a list of sources that have multiple images, and
# for each source we'll store the position in the source plane (beta) and
# the corresponding images in the image plane (thetas).
sources = [ ]
numSources = 50
while len(sources) < numSources:
# Pick a redshift that's somewhat larger than the lens's, but limit it
# to 4
zs = np.random.uniform(zd*1.2, 4.0)
# Obtain the relevant angular diameter distances for the source
Dds = cosm.getAngularDiameterDistance(zd,zs)
Ds = cosm.getAngularDiameterDistance(zs)
# Create an ImagePlane instance for this source, which is basically just
# the LensPlane but which also takes the source's angular diameter distances
# into account
imgplane = images.ImagePlane(lensplane, Ds, Dds)
# Pick a point in the source plane, and trace it to the image plane
beta = np.random.uniform(-40,40, 2) * ANGLE_ARCSEC
thetas = imgplane.traceBeta(beta)
# If we find that it has multiple images, we're going to add it to our list
if len(thetas) > 1:
sources.append({
"z": zs,
"Ds": Ds,
"Dds": Dds,
"beta": beta,
"thetas": thetas
})
```
```python
# Based on each beta and its corresponding thetas, we're going to create
# a list of points (in the image plane) and the deflection angles that the
# lens we're about to create should have at those points
points = [ ]
deflections = [ ]
for s in sources:
beta, Ds, Dds = s["beta"], s["Ds"], s["Dds"]
for t in s["thetas"]:
# The deflection angle is the difference between theta and beta
# but we need to take the source's distances into account as well
alpha = (t-beta)*Ds/Dds
points.append(t)
deflections.append(alpha)
# Set a scale for the Wendland basis functions; this one gives a nice result
scale = 500*ANGLE_ARCSEC
# Use the MultipleWendlandLens to build a lens that has these exact deflection
# angles at these points.
w = lenses.MultipleWendlandLens(Dd, {
"points": points,
"angles": deflections,
"scale": scale
})
```
```python
# Let's create another plot!
plotutil.plotDensityInteractive(LI(w, size=size));
```
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var options = {"width": "100%", "style": "surface", "showPerspective": true, "showGrid": true, "showShadow": false, "keepAspectRatio": true, "height": "600px", "xLabel": "X", "yLabel": "Y", "zLabel": "Z"};
var container = document.getElementById("visualization");
graph3d = new vis.Graph3d(container, data, options);
graph3d.on("cameraPositionChange", function(evt)
{
elem = document.getElementById("pos");
s = "horizontal: " + evt.horizontal.toExponential(4) + "<br>vertical: " + evt.vertical.toExponential(4) + "<br>distance: " + evt.distance.toExponential(4);
elem.innerHTML = s;
});
</script>
<button onclick="exportSVG()" style="position:fixed;top:0px;right:0px;">Save to SVG</button>
<script>
function T(x)
{
var s = "" + x;
while (s.length < 2)
s = "0" + s;
return s;
}
function exportSVG()
{
var cnvs = graph3d.frame.canvas;
var fakeCtx = C2S(cnvs.width, cnvs.height);
var realGetContext = cnvs.getContext;
cnvs.getContext = function() { return fakeCtx; }
graph3d.redraw();
var svg = fakeCtx.getSerializedSvg();
cnvs.getContext = realGetContext;
graph3d.redraw();
var b = new Blob([svg], { type: "image/svg+xml;charset=utf-8" });
var d = new Date();
var fileName = "Capture-" + d.getFullYear() + "-" + T(d.getMonth()+1) + "-" + T(d.getDate()) + "_" + T(d.getHours()) + "-" + T(d.getMinutes()) + "-" + T(d.getSeconds()) + ".svg";
saveAs(b, fileName);
}
</script>
</body>
</html>' width='100%' height='600px' style='border:0;' scrolling='no'> </iframe>
```python
# Looks nice, doesn't it? :-)
```
```python
```
|
j0r1REPO_NAMEGRALE2PATH_START.@GRALE2_extracted@GRALE2-master@pygrale@doc@source@_static@wendland.ipynb@.PATH_END.py
|
{
"filename": "_minexponent.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/layout/ternary/caxis/_minexponent.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class MinexponentValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="minexponent", parent_name="layout.ternary.caxis", **kwargs
):
super(MinexponentValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
min=kwargs.pop("min", 0),
role=kwargs.pop("role", "style"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@layout@ternary@caxis@_minexponent.py@.PATH_END.py
|
{
"filename": "TestEvidence.py",
"repo_name": "dokester/BayesicFitting",
"repo_path": "BayesicFitting_extracted/BayesicFitting-master/BayesicFitting/test/TestEvidence.py",
"type": "Python"
}
|
# run with : python3 -m unittest TestEvidence
import unittest
import os
import numpy as np
import math
from numpy.testing import assert_array_almost_equal as assertAAE
import matplotlib.pyplot as plt
from BayesicFitting import *
class TestEvidence( unittest.TestCase ) :
def __init__( self, testname ):
super( ).__init__( testname )
self.doplot = ( "DOPLOT" in os.environ and os.environ["DOPLOT"] == "1" )
def testEvidence( self ) :
print( "====testEvidence======================" )
nn = 100
x = np.arange( nn, dtype=float ) / (nn/2) - 1
ym = 1.2 + 0.5 * x
nf = 0.1
np.random.seed( 2345 )
noise = np.random.randn( nn )
y = ym + nf * noise
pm = PolynomialModel( 1 )
bf = Fitter( x, pm )
w = np.ones( nn, dtype=float )
pars = bf.fit( y, w )
print( "pars ", pars )
yfit = pm.result( x, pars )
print( "stdv ", bf.getStandardDeviations() )
bf.getHessian()
bf.chiSquared( y, weights=w )
print( "chisq %f scale %f sumwgt %f" % ( bf.chisq, bf.getScale(), bf.sumwgt ) )
lolim = [-100.0,-100.0]
hilim = [+100.0,+100.0]
nslim = [0.01, 100.0]
print( "=== Evidence for Parameters only; fixedScale is not set" )
print( "evid %f occam %f lhood %f fixedScale="%
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood ), bf.fixedScale )
if self.doplot :
plt.plot( x, ym, 'g.' )
plt.plot( x, y, 'b+' )
plt.plot( x, yfit, 'r-' )
plt.show()
print( "=== Evidence for Parameters and scale; fixedScale not set" )
print( "evid %f occam %f lhood %f fixedScale="%
( bf.getLogZ( limits=[lolim,hilim], noiseLimits=nslim ),
bf.logOccam, bf.logLikelihood ), bf.fixedScale )
# self.assertRaises( ValueError, bf.getLogZ() )
# self.assertRaises( ValueError, bf.logOccam )
# self.assertRaises( ValueError, bf.logLikelihood )
print( "=== Evidence for Parameters and scale; fixedScale=10" )
bf.fixedScale = 10.0
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim], noiseLimits=nslim ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
print( "=== Evidence for Parameters, fixed scale = 1" )
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
print( "=== Evidence for Parameters and fixed scale = 0.01" )
bf.fixedScale = 0.01
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
print( "=== Evidence for Parameters and fixed scale = 0.1" )
bf.fixedScale = 0.1
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
print( "=== Evidence for Parameters and fixed scale = 1.0" )
bf.fixedScale = 1.0
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
print( "=== Evidence for Parameters and fixed scale = 10.0" )
bf.fixedScale = 10.0
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( limits=[lolim,hilim] ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
pm.setLimits( lolim, hilim )
print( "limits par[0] ", pm.priors[0].lowLimit, pm.priors[0].highLimit )
print( "limits par[1] ", pm.priors[1].lowLimit, pm.priors[1].highLimit )
print( "=== Evidence for Parameters and fixed scale = 10.0" )
bf.fixedScale = 10.0
print( "evid %f occam %f lhood %f fixeScale=%f" %
( bf.getLogZ( ),
bf.logOccam, bf.logLikelihood, bf.fixedScale ) )
def testSimple1( self ) :
print( "====testEvidence for Gauss (Simple) ====" )
np.random.seed( 2345 )
nn = 100
x = np.arange( nn, dtype=int ) // 20
# ym = np.linspace( 1.0, 2.0, nn )
ym = 1.5
nf = 0.1
noise = np.random.normal( 0.0, 1.0, nn )
y = ym + nf * noise
pm = PolynomialModel( 0 )
print( pm.npchain )
bf = Fitter( x, pm, fixedScale=nf )
pars = bf.fit( y )
print( "pars ", pars )
yfit = pm.result( x, pars )
std = bf.stdevs
print( "stdv ", std )
print( "chisq %f scale %f sumwgt %f" % ( bf.chisq, bf.scale, bf.sumwgt ) )
print( bf.chiSquared( y, pars ) )
lo = 1.40
hi = 1.60
logz = bf.getLogZ( limits=[lo,hi] )
maxloglik = bf.logLikelihood
lintpr = math.log( hi - lo )
errdis = GaussErrorDistribution( scale=nf )
problem = ClassicProblem( model=pm, xdata=x, ydata=y )
npt = 401
p0 = np.linspace( lo, hi, npt )
for i in range( pm.npchain ) :
L0 = np.ndarray( npt, dtype=float )
pp = np.append( pars, [nf] )
for k,p in enumerate( p0 ) :
pp[i] = p
L0[k] = errdis.logLikelihood( problem, pp )
lz = np.log( np.sum( np.exp( L0 ) ) * (hi - lo) / npt )
maxl = np.max( L0 )
L0 -= maxloglik
if self.doplot :
gm = GaussModel()
gm.parameters = [1.0, pars[i], std[i]]
plt.plot( p0, gm( p0 ), 'b-' )
plt.plot( p0, np.exp( L0 ), 'k-' )
print( "BF logL ", maxloglik, bf.logOccam, maxl, np.exp( maxl ), lintpr )
print( "ED logL ", errdis.logLikelihood( problem, np.append( pars, [nf] ) ) )
print( "evid %f %f %f"%( logz, lz - lintpr,
maxloglik + math.log( 0.01 * math.pi ) - lintpr ) )
# print( "evid %f %f %f"%( logz, lz, maxloglik + math.log( 0.01 * math.pi ) ) )
if self.doplot :
plt.show()
def testSimpleLaplace1( self ) :
print( "====testEvidence for Laplace (Simple 1) ====" )
np.random.seed( 2345 )
nn = 1000
x = np.arange( nn, dtype=int ) // 20
ym = np.linspace( 1.0, 2.0, nn )
nf = 0.1
noise = np.random.laplace( 0.0, 1.0, nn )
y = ym + nf * noise
pm = PolynomialModel( 0 )
print( pm.npchain )
bf = PowellFitter( x, pm, scale=nf, errdis="laplace" )
pars = bf.fit( y, tolerance=1e-20 )
print( "pars ", pars )
yfit = pm.result( x, pars )
std = bf.stdevs
print( "stdv ", std )
print( np.median( y ), np.mean( y ) )
print( "scale ", bf.fixedScale )
print( "scale %f sumwgt %f" % ( bf.scale, bf.sumwgt ) )
lo = 1.45
hi = 1.55
logz = bf.getLogZ( limits=[lo,hi] )
maxloglik = bf.logLikelihood
lintpr = math.log( hi - lo )
errdis = LaplaceErrorDistribution( scale=nf )
problem = ClassicProblem( model=pm, xdata=x, ydata=y )
npt = 101
p0 = np.linspace( lo, hi, npt )
for i in range( pm.npchain ) :
L0 = np.ndarray( npt, dtype=float )
pp = np.append( pars, [nf] )
for k,p in enumerate( p0 ) :
pp[i] = p
L0[k] = errdis.logLikelihood( problem, pp )
lz = np.log( np.sum( np.exp( L0 ) ) * (hi - lo) / npt )
maxl = np.max( L0 )
L0 -= maxl
if self.doplot :
gm = GaussModel()
gm.parameters = [1.0, pars[i], std[i]]
# gm.parameters = [1.0, pars[i], 2*nf/math.sqrt(nn) ]
plt.plot( p0, gm( p0 ), 'b-' )
plt.plot( p0, np.exp( L0 ), 'k-' )
print( "BF logL ", maxloglik, bf.logOccam, maxl, np.exp( maxl ), lintpr )
print( "ED logL ", errdis.logLikelihood( problem, np.append( pars, [nf] ) ) )
print( "evid %f %f %f"%( logz, lz - lintpr,
maxloglik + math.log( 0.01 * math.pi ) - lintpr ) )
# print( "evid %f %f %f"%( logz, lz, maxloglik + math.log( 0.01 * math.pi ) ) )
if self.doplot :
plt.show()
def testGauss( self ) :
print( "====testEvidence for Gauss============" )
nn = 1000
x = np.arange( nn, dtype=int ) // 20
ym = np.linspace( 1.0, 2.0, nn )
nf = 0.1
np.random.seed( 2345 )
noise = np.random.normal( 0.0, 1.0, nn )
y = ym + nf * noise
# pm = FreeShapeModel( nn // 20 )
pm = PolynomialModel( 1 )
bf = AmoebaFitter( x, pm, errdis="gauss" )
pars = bf.fit( y )
print( "pars ", pars )
yfit = pm.result( x, pars )
std = bf.stdevs
print( "stdv ", std )
print( "scale %f sumwgt %f" % ( bf.scale, bf.sumwgt ) )
errdis = GaussErrorDistribution( scale=nf )
problem = ClassicProblem( model=pm, xdata=x, ydata=y )
p0 = np.linspace( 0.0, 3.0, 301 )
for i in range( pm.npchain ) :
L0 = np.ndarray( 301, dtype=float )
pp = np.append( pars, [0.1] )
for k,p in enumerate( p0 ) :
pp[i] = p
L0[k] = errdis.logLikelihood( problem, pp )
L0 -= np.max( L0 )
if self.doplot :
gm = GaussModel()
gm.parameters = [1.0, pars[i], std[i]]
plt.plot( p0, gm( p0 ), 'b-' )
plt.plot( p0, np.exp( L0 ), 'k-' )
if self.doplot :
plt.show()
def testLaplace( self ) :
print( "====testEvidence for Laplace============" )
nn = 100
x = np.arange( nn, dtype=float ) / (nn/2) - 1
ym = 1.3
nf = 0.1
np.random.seed( 2345 )
noise = np.random.laplace( 0.0, 1.0, nn )
y = ym + nf * noise
pm = PolynomialModel( 0 )
bf = PowellFitter( x, pm, errdis="laplace" )
pars = bf.fit( y, tolerance=1e-10 )
print( "pars ", pars )
yfit = pm.result( x, pars )
std = bf.stdevs
print( "stdv ", std )
print( "scale %f sumwgt %f" % ( bf.scale, bf.sumwgt ) )
errdis = LaplaceErrorDistribution( scale=nf )
problem = ClassicProblem( model=pm, xdata=x, ydata=y )
p0 = np.linspace( 1.2, 1.5, 201 )
L0 = np.ndarray( 201, dtype=float )
for k,p in enumerate( p0 ) :
pp = [p, 0.1]
L0[k] = errdis.logLikelihood( problem, pp )
L0 -= np.max( L0 )
if self.doplot :
gm = GaussModel()
gm.parameters = [1.0, pars[0], std[0]]
plt.plot( p0, gm( p0 ), 'b-' )
plt.plot( p0, np.exp( L0 ), 'k-' )
plt.show()
if __name__ == '__main__':
unittest.main( )
|
dokesterREPO_NAMEBayesicFittingPATH_START.@BayesicFitting_extracted@BayesicFitting-master@BayesicFitting@test@TestEvidence.py@.PATH_END.py
|
{
"filename": "cosmo_interp.py",
"repo_name": "sibirrer/lenstronomy",
"repo_path": "lenstronomy_extracted/lenstronomy-main/lenstronomy/Cosmo/cosmo_interp.py",
"type": "Python"
}
|
import astropy
from lenstronomy.Util.util import isiterable
if float(astropy.__version__[0]) < 5.0:
from astropy.cosmology.core import isiterable
DeprecationWarning(
"Astropy<5 is going to be deprecated soon. This is in combination with Python version<3.8."
"We recommend you to update astropy to the latest version but keep supporting your settings for "
"the time being."
)
# elif float(astropy.__version__[0]) < 6.0:
# from astropy.cosmology.utils import isiterable
# else:
# from astropy.cosmology.utils.misc import isiterable
#
from astropy import units
import numpy as np
import copy
from scipy.interpolate import interp1d
class CosmoInterp(object):
"""Class which interpolates the comoving transfer distance and then computes angular
diameter distances from it This class is modifying the astropy.cosmology
routines."""
def __init__(
self,
cosmo=None,
z_stop=None,
num_interp=None,
ang_dist_list=None,
z_list=None,
Ok0=None,
K=None,
):
"""
:param cosmo: astropy.cosmology instance (version 4.0 as private functions need to be supported)
:param z_stop: maximum redshift for the interpolation
:param num_interp: int, number of interpolating steps
:param ang_dist_list: array of angular diameter distances in Mpc to be interpolated (optional)
:param z_list: list of redshifts corresponding to ang_dist_list (optional)
:param Ok0: Omega_k(z=0)
:param K: Omega_k / (hubble distance)^2 in Mpc^-2
"""
if cosmo is None:
if Ok0 is None:
Ok0 = 0
K = 0
self.Ok0 = Ok0
self.k = K / units.Mpc**2 # in units inverse Mpc^2
self._comoving_distance_interpolation_func = self._interpolate_ang_dist(
ang_dist_list, z_list, self.Ok0, self.k.value
)
else:
self._cosmo = cosmo
self.Ok0 = self._cosmo._Ok0
dh = self._cosmo._hubble_distance
self.k = -self.Ok0 / dh**2
if float(astropy.__version__[0]) < 5.0:
from lenstronomy.Cosmo._cosmo_interp_astropy_v4 import (
CosmoInterp as CosmoInterp_,
)
self._comoving_interp = CosmoInterp_(cosmo)
else:
from lenstronomy.Cosmo._cosmo_interp_astropy_v5 import (
CosmoInterp as CosmoInterp_,
)
self._comoving_interp = CosmoInterp_(cosmo)
self._comoving_distance_interpolation_func = (
self._interpolate_comoving_distance(
z_start=0, z_stop=z_stop, num_interp=num_interp
)
)
self._abs_sqrt_k = np.sqrt(abs(self.k))
def _comoving_distance_interp(self, z):
"""
:param z: redshift to which the comoving distance is calculated
:return: comoving distance in units Mpc
"""
return self._comoving_distance_interpolation_func(z) * units.Mpc
def angular_diameter_distance(self, z):
"""Angular diameter distance in Mpc at a given redshift.
This gives the proper (sometimes called 'physical') transverse
distance corresponding to an angle of 1 radian for an object
at redshift ``z``.
Weinberg, 1972, pp 421-424; Weedman, 1986, pp 65-67; Peebles,
1993, pp 325-327.
Parameters
----------
z : array_like
Input redshifts. Must be 1D or scalar.
Returns
-------
d : `~astropy.units.Quantity`
Angular diameter distance in Mpc at each input redshift.
"""
if isiterable(z):
z = np.asarray(z)
return self.comoving_transverse_distance(z) / (1.0 + z)
def angular_diameter_distance_z1z2(self, z1, z2):
"""Angular diameter distance between objects at 2 redshifts. Useful for
gravitational lensing.
Parameters
----------
z1, z2 : array_like, shape (N,)
Input redshifts. z2 must be large than z1.
Returns
-------
d : `~astropy.units.Quantity`, shape (N,) or single if input scalar
The angular diameter distance between each input redshift
pair.
"""
z1 = np.asanyarray(z1)
z2 = np.asanyarray(z2)
return self._comoving_transverse_distance_z1z2(z1, z2) / (1.0 + z2)
def comoving_transverse_distance(self, z):
"""Comoving transverse distance in Mpc at a given redshift.
This value is the transverse comoving distance at redshift ``z``
corresponding to an angular separation of 1 radian. This is
the same as the comoving distance if omega_k is zero (as in
the current concordance lambda CDM model).
Parameters
----------
z : array_like
Input redshifts. Must be 1D or scalar.
Returns
-------
d : `~astropy.units.Quantity`
Comoving transverse distance in Mpc at each input redshift.
Notes
-----
This quantity also called the 'proper motion distance' in some
texts.
"""
return self._comoving_transverse_distance_z1z2(0, z)
def _comoving_transverse_distance_z1z2(self, z1, z2):
"""Comoving transverse distance in Mpc between two redshifts.
This value is the transverse comoving distance at redshift
``z2`` as seen from redshift ``z1`` corresponding to an
angular separation of 1 radian. This is the same as the
comoving distance if omega_k is zero (as in the current
concordance lambda CDM model).
Parameters
----------
z1, z2 : array_like, shape (N,)
Input redshifts. Must be 1D or scalar.
Returns
-------
d : `~astropy.units.Quantity`
Comoving transverse distance in Mpc between input redshift.
Notes
-----
This quantity is also called the 'proper motion distance' in
some texts.
"""
dc = self._comoving_distance_z1z2(z1, z2)
if np.fabs(self.Ok0) < 1.0e-6:
return dc
elif self.k < 0:
return 1.0 / self._abs_sqrt_k * np.sinh(self._abs_sqrt_k.value * dc.value)
else:
return 1.0 / self._abs_sqrt_k * np.sin(self._abs_sqrt_k.value * dc.value)
def _comoving_distance_z1z2(self, z1, z2):
"""Comoving line-of-sight distance in Mpc between objects at redshifts z1 and
z2.
The comoving distance along the line-of-sight between two
objects remains constant with time for objects in the Hubble
flow.
Parameters
----------
z1, z2 : array_like, shape (N,)
Input redshifts. Must be 1D or scalar.
Returns
-------
d : `~astropy.units.Quantity`
Comoving distance in Mpc between each input redshift.
"""
return self._comoving_distance_interp(z2) - self._comoving_distance_interp(z1)
def _interpolate_comoving_distance(self, z_start, z_stop, num_interp):
"""Interpolates the comoving distance.
:param z_start: starting redshift range (should be zero)
:param z_stop: highest redshift to which to compute the comoving distance
:param num_interp: number of steps uniformly spread in redshift
:return: interpolation object in this class
"""
z_steps = np.linspace(start=z_start, stop=z_stop, num=num_interp + 1)
running_dist = 0
ang_dist = np.zeros(num_interp + 1)
for i in range(num_interp):
delta_dist = self._comoving_interp._integral_comoving_distance_z1z2(
z_steps[i], z_steps[i + 1]
)
running_dist += delta_dist.value
ang_dist[i + 1] = copy.deepcopy(running_dist)
return interp1d(z_steps, ang_dist)
def _interpolate_ang_dist(self, ang_dist_list, z_list, Ok0, K):
"""Translates angular diameter distances to transversal comoving distances.
:param ang_dist_list: angular diameter distances in units Mpc
:type ang_dist_list: numpy array
:param z_list: redshifts corresponding to ang_dist_list
:type z_list: numpy array
:param Ok0: Omega_k(z=0)
:param K: Omega_k / (hubble distance)^2 in Mpc^-2
:return: interpolation function of transversal comoving diameter distance [Mpc]
"""
ang_dist_list = np.asanyarray(ang_dist_list)
z_list = np.asanyarray(z_list)
if z_list[0] > 0: # if redshift zero is not in input, add it
z_list = np.append(0, z_list)
ang_dist_list = np.append(0, ang_dist_list)
if np.fabs(Ok0) < 1.0e-6:
comoving_dist_list = ang_dist_list * (1.0 + z_list)
elif K < 0:
comoving_dist_list = np.arcsinh(
ang_dist_list * (1.0 + z_list) * np.sqrt(-K)
) / np.sqrt(-K)
else:
comoving_dist_list = np.arcsin(
ang_dist_list * (1.0 + z_list) * np.sqrt(K)
) / np.sqrt(K)
return interp1d(z_list, comoving_dist_list)
|
sibirrerREPO_NAMElenstronomyPATH_START.@lenstronomy_extracted@lenstronomy-main@lenstronomy@Cosmo@cosmo_interp.py@.PATH_END.py
|
{
"filename": "_line.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattergeo/marker/_line.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LineValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="line", parent_name="scattergeo.marker", **kwargs):
super(LineValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Line"),
data_docs=kwargs.pop(
"data_docs",
"""
autocolorscale
Determines whether the colorscale is a default
palette (`autocolorscale: true`) or the palette
determined by `marker.line.colorscale`. Has an
effect only if in `marker.line.color` is set to
a numerical array. In case `colorscale` is
unspecified or `autocolorscale` is true, the
default palette will be chosen according to
whether numbers in the `color` array are all
positive, all negative or mixed.
cauto
Determines whether or not the color domain is
computed with respect to the input data (here
in `marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has
an effect only if in `marker.line.color` is set
to a numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are
set by the user.
cmax
Sets the upper bound of the color domain. Has
an effect only if in `marker.line.color` is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmin` must be set as well.
cmid
Sets the mid-point of the color domain by
scaling `marker.line.cmin` and/or
`marker.line.cmax` to be equidistant to this
point. Has an effect only if in
`marker.line.color` is set to a numerical
array. Value should have the same units as in
`marker.line.color`. Has no effect when
`marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has
an effect only if in `marker.line.color` is set
to a numerical array. Value should have the
same units as in `marker.line.color` and if
set, `marker.line.cmax` must be set as well.
color
Sets the marker.line color. It accepts either a
specific color or an array of numbers that are
mapped to the colorscale relative to the max
and min values of the array or relative to
`marker.line.cmin` and `marker.line.cmax` if
set.
coloraxis
Sets a reference to a shared color axis.
References to these shared color axes are
"coloraxis", "coloraxis2", "coloraxis3", etc.
Settings for these shared color axes are set in
the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple
color scales can be linked to the same color
axis.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color` is set to a numerical
array. The colorscale must be an array
containing arrays mapping a normalized value to
an rgb, rgba, hex, hsl, hsv, or named color
string. At minimum, a mapping for the lowest
(0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the
colorscale in color space, use
`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette
name string of the following list: Blackbody,Bl
uered,Blues,Cividis,Earth,Electric,Greens,Greys
,Hot,Jet,Picnic,Portland,Rainbow,RdBu,Reds,Viri
dis,YlGnBu,YlOrRd.
colorsrc
Sets the source reference on Chart Studio Cloud
for `color`.
reversescale
Reverses the color mapping if true. Has an
effect only if in `marker.line.color` is set to
a numerical array. If true, `marker.line.cmin`
will correspond to the last color in the array
and `marker.line.cmax` will correspond to the
first color.
width
Sets the width (in px) of the lines bounding
the marker points.
widthsrc
Sets the source reference on Chart Studio Cloud
for `width`.
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattergeo@marker@_line.py@.PATH_END.py
|
{
"filename": "sparkllm.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/llms/sparkllm.py",
"type": "Python"
}
|
from __future__ import annotations
import base64
import hashlib
import hmac
import json
import logging
import queue
import threading
from datetime import datetime
from queue import Queue
from time import mktime
from typing import Any, Dict, Generator, Iterator, List, Optional
from urllib.parse import urlencode, urlparse, urlunparse
from wsgiref.handlers import format_date_time
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.utils import get_from_dict_or_env, pre_init
from pydantic import Field
logger = logging.getLogger(__name__)
class SparkLLM(LLM):
"""iFlyTek Spark completion model integration.
Setup:
To use, you should set environment variables ``IFLYTEK_SPARK_APP_ID``,
``IFLYTEK_SPARK_API_KEY`` and ``IFLYTEK_SPARK_API_SECRET``.
.. code-block:: bash
export IFLYTEK_SPARK_APP_ID="your-app-id"
export IFLYTEK_SPARK_API_KEY="your-api-key"
export IFLYTEK_SPARK_API_SECRET="your-api-secret"
Key init args — completion params:
model: Optional[str]
Name of IFLYTEK SPARK model to use.
temperature: Optional[float]
Sampling temperature.
top_k: Optional[float]
What search sampling control to use.
streaming: Optional[bool]
Whether to stream the results or not.
Key init args — client params:
app_id: Optional[str]
IFLYTEK SPARK API KEY. Automatically inferred from env var `IFLYTEK_SPARK_APP_ID` if not provided.
api_key: Optional[str]
IFLYTEK SPARK API KEY. If not passed in will be read from env var IFLYTEK_SPARK_API_KEY.
api_secret: Optional[str]
IFLYTEK SPARK API SECRET. If not passed in will be read from env var IFLYTEK_SPARK_API_SECRET.
api_url: Optional[str]
Base URL for API requests.
timeout: Optional[int]
Timeout for requests.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_community.llms import SparkLLM
llm = SparkLLM(
app_id="your-app-id",
api_key="your-api_key",
api_secret="your-api-secret",
# model='Spark4.0 Ultra',
# temperature=...,
# other params...
)
Invoke:
.. code-block:: python
input_text = "用50个字左右阐述，生命的意义在于"
llm.invoke(input_text)
.. code-block:: python
'生命的意义在于实现自我价值，追求内心的平静与快乐，同时为他人和社会带来正面影响。'
Stream:
.. code-block:: python
for chunk in llm.stream(input_text):
print(chunk)
.. code-block:: python
生命 | 的意义在于 | 不断探索和 | 实现个人潜能，通过 | 学习 | 、成长和对社会 | 的贡献，追求内心的满足和幸福。
Async:
.. code-block:: python
await llm.ainvoke(input_text)
# stream:
# async for chunk in llm.astream(input_text):
# print(chunk)
# batch:
# await llm.abatch([input_text])
.. code-block:: python
'生命的意义在于实现自我价值，追求内心的平静与快乐，同时为他人和社会带来正面影响。'
""" # noqa: E501
client: Any = None #: :meta private:
spark_app_id: Optional[str] = Field(default=None, alias="app_id")
"""Automatically inferred from env var `IFLYTEK_SPARK_APP_ID`
if not provided."""
spark_api_key: Optional[str] = Field(default=None, alias="api_key")
"""IFLYTEK SPARK API KEY. If not passed in will be read from
env var IFLYTEK_SPARK_API_KEY."""
spark_api_secret: Optional[str] = Field(default=None, alias="api_secret")
"""IFLYTEK SPARK API SECRET. If not passed in will be read from
env var IFLYTEK_SPARK_API_SECRET."""
spark_api_url: Optional[str] = Field(default=None, alias="api_url")
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
spark_llm_domain: Optional[str] = Field(default=None, alias="model")
"""Model name to use."""
spark_user_id: str = "lc_user"
streaming: bool = False
"""Whether to stream the results or not."""
request_timeout: int = Field(default=30, alias="timeout")
"""request timeout for chat http requests"""
temperature: float = 0.5
"""What sampling temperature to use."""
top_k: int = 4
"""What search sampling control to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for API call not explicitly specified."""
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
values["spark_app_id"] = get_from_dict_or_env(
values,
["spark_app_id", "app_id"],
"IFLYTEK_SPARK_APP_ID",
)
values["spark_api_key"] = get_from_dict_or_env(
values,
["spark_api_key", "api_key"],
"IFLYTEK_SPARK_API_KEY",
)
values["spark_api_secret"] = get_from_dict_or_env(
values,
["spark_api_secret", "api_secret"],
"IFLYTEK_SPARK_API_SECRET",
)
values["spark_api_url"] = get_from_dict_or_env(
values,
["spark_api_url", "api_url"],
"IFLYTEK_SPARK_API_URL",
"wss://spark-api.xf-yun.com/v3.5/chat",
)
values["spark_llm_domain"] = get_from_dict_or_env(
values,
["spark_llm_domain", "model"],
"IFLYTEK_SPARK_LLM_DOMAIN",
"generalv3.5",
)
# put extra params into model_kwargs
values["model_kwargs"]["temperature"] = values["temperature"] or cls.temperature
values["model_kwargs"]["top_k"] = values["top_k"] or cls.top_k
values["client"] = _SparkLLMClient(
app_id=values["spark_app_id"],
api_key=values["spark_api_key"],
api_secret=values["spark_api_secret"],
api_url=values["spark_api_url"],
spark_domain=values["spark_llm_domain"],
model_kwargs=values["model_kwargs"],
)
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "spark-llm-chat"
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling SparkLLM API."""
normal_params = {
"spark_llm_domain": self.spark_llm_domain,
"stream": self.streaming,
"request_timeout": self.request_timeout,
"top_k": self.top_k,
"temperature": self.temperature,
}
return {**normal_params, **self.model_kwargs}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to an sparkllm for each generation with a prompt.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the llm.
Example:
.. code-block:: python
response = client("Tell me a joke.")
"""
if self.streaming:
completion = ""
for chunk in self._stream(prompt, stop, run_manager, **kwargs):
completion += chunk.text
return completion
completion = ""
self.client.arun(
[{"role": "user", "content": prompt}],
self.spark_user_id,
self.model_kwargs,
self.streaming,
)
for content in self.client.subscribe(timeout=self.request_timeout):
if "data" not in content:
continue
completion = content["data"]["content"]
return completion
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
self.client.run(
[{"role": "user", "content": prompt}],
self.spark_user_id,
self.model_kwargs,
True,
)
for content in self.client.subscribe(timeout=self.request_timeout):
if "data" not in content:
continue
delta = content["data"]
if run_manager:
run_manager.on_llm_new_token(delta)
yield GenerationChunk(text=delta["content"])
class _SparkLLMClient:
"""
Use websocket-client to call the SparkLLM interface provided by Xfyun,
which is the iFlyTek's open platform for AI capabilities
"""
def __init__(
self,
app_id: str,
api_key: str,
api_secret: str,
api_url: Optional[str] = None,
spark_domain: Optional[str] = None,
model_kwargs: Optional[dict] = None,
):
try:
import websocket
self.websocket_client = websocket
except ImportError:
raise ImportError(
"Could not import websocket client python package. "
"Please install it with `pip install websocket-client`."
)
self.api_url = (
"wss://spark-api.xf-yun.com/v3.5/chat" if not api_url else api_url
)
self.app_id = app_id
self.model_kwargs = model_kwargs
self.spark_domain = spark_domain or "generalv3.5"
self.queue: Queue[Dict] = Queue()
self.blocking_message = {"content": "", "role": "assistant"}
self.api_key = api_key
self.api_secret = api_secret
@staticmethod
def _create_url(api_url: str, api_key: str, api_secret: str) -> str:
"""
Generate a request url with an api key and an api secret.
"""
# generate timestamp by RFC1123
date = format_date_time(mktime(datetime.now().timetuple()))
# urlparse
parsed_url = urlparse(api_url)
host = parsed_url.netloc
path = parsed_url.path
signature_origin = f"host: {host}\ndate: {date}\nGET {path} HTTP/1.1"
# encrypt using hmac-sha256
signature_sha = hmac.new(
api_secret.encode("utf-8"),
signature_origin.encode("utf-8"),
digestmod=hashlib.sha256,
).digest()
signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", \
headers="host date request-line", signature="{signature_sha_base64}"'
authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
encoding="utf-8"
)
# generate url
params_dict = {"authorization": authorization, "date": date, "host": host}
encoded_params = urlencode(params_dict)
url = urlunparse(
(
parsed_url.scheme,
parsed_url.netloc,
parsed_url.path,
parsed_url.params,
encoded_params,
parsed_url.fragment,
)
)
return url
def run(
self,
messages: List[Dict],
user_id: str,
model_kwargs: Optional[dict] = None,
streaming: bool = False,
) -> None:
self.websocket_client.enableTrace(False)
ws = self.websocket_client.WebSocketApp(
_SparkLLMClient._create_url(
self.api_url,
self.api_key,
self.api_secret,
),
on_message=self.on_message,
on_error=self.on_error,
on_close=self.on_close,
on_open=self.on_open,
)
ws.messages = messages # type: ignore[attr-defined]
ws.user_id = user_id # type: ignore[attr-defined]
ws.model_kwargs = self.model_kwargs if model_kwargs is None else model_kwargs # type: ignore[attr-defined]
ws.streaming = streaming # type: ignore[attr-defined]
ws.run_forever()
def arun(
self,
messages: List[Dict],
user_id: str,
model_kwargs: Optional[dict] = None,
streaming: bool = False,
) -> threading.Thread:
ws_thread = threading.Thread(
target=self.run,
args=(
messages,
user_id,
model_kwargs,
streaming,
),
)
ws_thread.start()
return ws_thread
def on_error(self, ws: Any, error: Optional[Any]) -> None:
self.queue.put({"error": error})
ws.close()
def on_close(self, ws: Any, close_status_code: int, close_reason: str) -> None:
logger.debug(
{
"log": {
"close_status_code": close_status_code,
"close_reason": close_reason,
}
}
)
self.queue.put({"done": True})
def on_open(self, ws: Any) -> None:
self.blocking_message = {"content": "", "role": "assistant"}
data = json.dumps(
self.gen_params(
messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs
)
)
ws.send(data)
def on_message(self, ws: Any, message: str) -> None:
data = json.loads(message)
code = data["header"]["code"]
if code != 0:
self.queue.put(
{"error": f"Code: {code}, Error: {data['header']['message']}"}
)
ws.close()
else:
choices = data["payload"]["choices"]
status = choices["status"]
content = choices["text"][0]["content"]
if ws.streaming:
self.queue.put({"data": choices["text"][0]})
else:
self.blocking_message["content"] += content
if status == 2:
if not ws.streaming:
self.queue.put({"data": self.blocking_message})
usage_data = (
data.get("payload", {}).get("usage", {}).get("text", {})
if data
else {}
)
self.queue.put({"usage": usage_data})
ws.close()
def gen_params(
self, messages: list, user_id: str, model_kwargs: Optional[dict] = None
) -> dict:
data: Dict = {
"header": {"app_id": self.app_id, "uid": user_id},
"parameter": {"chat": {"domain": self.spark_domain}},
"payload": {"message": {"text": messages}},
}
if model_kwargs:
data["parameter"]["chat"].update(model_kwargs)
logger.debug(f"Spark Request Parameters: {data}")
return data
def subscribe(self, timeout: Optional[int] = 30) -> Generator[Dict, None, None]:
while True:
try:
content = self.queue.get(timeout=timeout)
except queue.Empty as _:
raise TimeoutError(
f"SparkLLMClient wait LLM api response timeout {timeout} seconds"
)
if "error" in content:
raise ConnectionError(content["error"])
if "usage" in content:
yield content
continue
if "done" in content:
break
if "data" not in content:
break
yield content
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@llms@sparkllm.py@.PATH_END.py
|
{
"filename": "_font.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/graph_objs/scattercarpet/legendgrouptitle/_font.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Font(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "scattercarpet.legendgrouptitle"
_path_str = "scattercarpet.legendgrouptitle.font"
_valid_props = {
"color",
"family",
"lineposition",
"shadow",
"size",
"style",
"textcase",
"variant",
"weight",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# family
# ------
@property
def family(self):
"""
HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans", "Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow",
"Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
Returns
-------
str
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# lineposition
# ------------
@property
def lineposition(self):
"""
Sets the kind of decoration line(s) with text, such as an
"under", "over" or "through" as well as combinations e.g.
"under+over", etc.
The 'lineposition' property is a flaglist and may be specified
as a string containing:
- Any combination of ['under', 'over', 'through'] joined with '+' characters
(e.g. 'under+over')
OR exactly one of ['none'] (e.g. 'none')
Returns
-------
Any
"""
return self["lineposition"]
@lineposition.setter
def lineposition(self, val):
self["lineposition"] = val
# shadow
# ------
@property
def shadow(self):
"""
Sets the shape and color of the shadow behind text. "auto"
places minimal shadow and applies contrast text font color. See
https://developer.mozilla.org/en-US/docs/Web/CSS/text-shadow
for additional options.
The 'shadow' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["shadow"]
@shadow.setter
def shadow(self, val):
self["shadow"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
Returns
-------
int|float
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
Returns
-------
Any
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# textcase
# --------
@property
def textcase(self):
"""
Sets capitalization of text. It can be used to make text appear
in all-uppercase or all-lowercase, or with each word
capitalized.
The 'textcase' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'word caps', 'upper', 'lower']
Returns
-------
Any
"""
return self["textcase"]
@textcase.setter
def textcase(self, val):
self["textcase"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps', 'all-small-caps',
'all-petite-caps', 'petite-caps', 'unicase']
Returns
-------
Any
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [1, 1000]
OR exactly one of ['normal', 'bold'] (e.g. 'bold')
Returns
-------
int
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
"""
def __init__(
self,
arg=None,
color=None,
family=None,
lineposition=None,
shadow=None,
size=None,
style=None,
textcase=None,
variant=None,
weight=None,
**kwargs,
):
"""
Construct a new Font object
Sets this legend group's title font.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.scattercarpet.
legendgrouptitle.Font`
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
Returns
-------
Font
"""
super(Font, self).__init__("font")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.scattercarpet.legendgrouptitle.Font
constructor must be a dict or
an instance of :class:`plotly.graph_objs.scattercarpet.legendgrouptitle.Font`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("lineposition", None)
_v = lineposition if lineposition is not None else _v
if _v is not None:
self["lineposition"] = _v
_v = arg.pop("shadow", None)
_v = shadow if shadow is not None else _v
if _v is not None:
self["shadow"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("textcase", None)
_v = textcase if textcase is not None else _v
if _v is not None:
self["textcase"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@graph_objs@scattercarpet@legendgrouptitle@_font.py@.PATH_END.py
|
{
"filename": "dynesty_plots.py",
"repo_name": "CarlosCoba/XookSuut-code",
"repo_path": "XookSuut-code_extracted/XookSuut-code-master/src/dynesty_plots.py",
"type": "Python"
}
|
import matplotlib.pylab as plt
from dynesty import plotting as dyplot
from src.axes_params import axes_ambient as AX
from matplotlib.gridspec import GridSpec
from matplotlib import gridspec
height, width = 18.0, 14 # width [cm]
cm_to_inch = 0.393701 # [inch/cm]
figWidth = width * cm_to_inch # width [inch]
figHeight = height * cm_to_inch # width [inch]
def dplots(res,truths,vmode, galaxy, PropDist, int_scatter, n_circ, n_noncirc):
ndim=len(truths)
nrow, ncol = ndim, 2
labels = ["$v_{%s}$"%k for k in range(ndim)]
labels_const = ["$\mathrm{\phi^{\prime}}$", "$\epsilon$", "$\mathrm{x_0}$", "$\mathrm{y_0}$", "$\mathrm{V_{sys}}$"]
if "hrm" in vmode:
labels_const[-1] = "$c_0$"
if vmode == "bisymmetric":
labels_const.append("$\\phi_{\mathrm{bar}}$")
if PropDist == "G":
if int_scatter :
labels_const.append("$\mathrm{\ln~\sigma_{int}^2}$")
if PropDist == "C":
labels_const.append("$\mathrm{\gamma~(km/s)}$")
nconst = len(labels_const)
labels[-nconst:] = labels_const[:]
fig, axs = plt.subplots(nrow, ncol, figsize = (figWidth, figHeight), \
gridspec_kw={'height_ratios': [1.5 for k in range(nrow)], 'width_ratios': [4.5 for k in range(ncol)]})
# plotting the original run
dyplot.traceplot(res, truths=truths, truth_color="#faa022",
show_titles=True, title_kwargs={'fontsize': 12, 'y': 1.05},
trace_cmap='plasma', kde=False, max_n_ticks = 4, labels = labels,
fig=(fig, axs))
plt.gcf().subplots_adjust(bottom=0.1, top = 0.95)
plt.savefig("./XS/figures/%s.%s.dyplot.trace.png"%(galaxy,vmode),dpi=300)
plt.close()
plt.clf()
nrow, ncol = 4, 1
fig, axs = plt.subplots(nrow, ncol, figsize = ( 4, 6))
dyplot.runplot(res, color='dodgerblue', fig = (fig, axs))
plt.gcf().subplots_adjust(bottom=0.1, top = 0.95, left = 0.2, right = 0.98)
#fig.tight_layout()
plt.savefig("./XS/figures/%s.%s.dyplot.runplot.png"%(galaxy,vmode))
plt.close()
plt.clf()
|
CarlosCobaREPO_NAMEXookSuut-codePATH_START.@XookSuut-code_extracted@XookSuut-code-master@src@dynesty_plots.py@.PATH_END.py
|
{
"filename": "wrapper_xspec_examples.py",
"repo_name": "atomdb/pyatomdb",
"repo_path": "pyatomdb_extracted/pyatomdb-master/pyatomdb/examples/wrapper_xspec_examples.py",
"type": "Python"
}
|
from apec_xspec import *
# declare a new model
# inital import creates pyapec, pyvapec, pyvvapec, analagous to apec, vapec, vvapec
m1 = xspec.Model('pyapec')
m1.show()
m1.pyapec.kT=5.0
# let's plot a spectrum
xspec.Plot.device='/xs'
xspec.Plot('model')
# you can fiddle with some aspects of the model directly
cie.set_eebrems(False) # turn off electron-electron bremsstrahlung
xspec.Plot('model')
# it is also possible to go in and change broadening, etc. The interface can be
# adjust quite easily by looking at apec_xspec.py to add extra parameters
|
atomdbREPO_NAMEpyatomdbPATH_START.@pyatomdb_extracted@pyatomdb-master@pyatomdb@examples@wrapper_xspec_examples.py@.PATH_END.py
|
{
"filename": "n2dp.py",
"repo_name": "vlas-sokolov/pyspecnest",
"repo_path": "pyspecnest_extracted/pyspecnest-master/pyspecnest/n2dp.py",
"type": "Python"
}
|
import os
import numpy as np
from .multiwrapper import Parameter, ModelContainer
# TODO: generalize it with the parameter names already present in pyspeckit!
def get_n2dp_model(sp, std_noise, priors=None, npeaks=1, **kwargs):
# initializing the model parameters
if priors is None:
# set up dummy priors for an example run
# FIXME: the popping techninque, amazing as
# it is, is merely an ugly hack!
# priors should be initialized from a dict
priors = [[3, 20], [0, 30], [-30, 30], [0, 1]][::-1] * npeaks
parlist = []
for i in range(npeaks):
tex = Parameter("tex_{}".format(i),
r'$\mathrm{{T_{{ex{}}}}}$'.format(i), priors.pop())
tau = Parameter("tau_{}".format(i), r'$\mathrm{{\tau_{}}}$'.format(i),
priors.pop())
xoff = Parameter("xoff_{}".format(i),
r'$\mathrm{{x_{{off{}}}}}$'.format(i), priors.pop())
sig = Parameter("sig_{}".format(i), r'$\sigma_{}$'.format(i),
priors.pop())
parlist += [tex, tau, xoff, sig]
n2dp_model = ModelContainer(
parlist,
model=sp.specfit.get_full_model,
std_noise=std_noise,
xdata=sp.xarr.value,
ydata=sp.data,
npeaks=npeaks,
**kwargs)
return n2dp_model
def suffix_str(model, snr):
""" Name id for output files """
fixed_str = ''.join([{True: 'T', False: 'F'}[i] for i in model.fixed])
out_suffix = '{}_snr{:n}'.format(fixed_str, snr)
return out_suffix
def get_pymultinest_dir(output_dir, prefix, suffix, subdir='chains'):
""" Sets up and returns multinest output directory """
local_dir = '{}/{}_{}/'.format(subdir, prefix, suffix)
pymultinest_output = os.path.join(output_dir, local_dir)
if not os.path.exists(pymultinest_output):
os.mkdir(pymultinest_output)
return pymultinest_output
|
vlas-sokolovREPO_NAMEpyspecnestPATH_START.@pyspecnest_extracted@pyspecnest-master@pyspecnest@n2dp.py@.PATH_END.py
|
{
"filename": "logger.py",
"repo_name": "astropy/astropy",
"repo_path": "astropy_extracted/astropy-main/astropy/logger.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""This module defines a logging class based on the built-in logging module.
.. note::
This module is meant for internal ``astropy`` usage. For use in other
packages, we recommend implementing your own logger instead.
"""
import inspect
import logging
import sys
import warnings
from contextlib import contextmanager
from logging import CRITICAL, DEBUG, ERROR, FATAL, INFO, NOTSET, WARNING
from pathlib import Path
from . import conf as _conf
from . import config as _config
from .utils import find_current_module
from .utils.exceptions import AstropyUserWarning, AstropyWarning
try:
# this name is only defined if running within ipython/jupyter
__IPYTHON__ # noqa: B018
except NameError:
_WITHIN_IPYTHON = False
else:
_WITHIN_IPYTHON = True
__all__ = [
"CRITICAL",
"DEBUG",
"ERROR",
"FATAL",
"INFO",
# import the logging levels from logging so that one can do:
# log.setLevel(log.DEBUG), for example
"NOTSET",
"WARNING",
"AstropyLogger",
"Conf",
"LoggingError",
"conf",
"log",
]
# Initialize by calling _init_log()
log = None
class LoggingError(Exception):
"""
This exception is for various errors that occur in the astropy logger,
typically when activating or deactivating logger-related features.
"""
class _AstLogIPYExc(Exception):
"""
An exception that is used only as a placeholder to indicate to the
IPython exception-catching mechanism that the astropy
exception-capturing is activated. It should not actually be used as
an exception anywhere.
"""
class Conf(_config.ConfigNamespace):
"""
Configuration parameters for `astropy.logger`.
"""
log_level = _config.ConfigItem(
"INFO",
"Threshold for the logging messages. Logging "
"messages that are less severe than this level "
"will be ignored. The levels are ``'DEBUG'``, "
"``'INFO'``, ``'WARNING'``, ``'ERROR'``.",
)
log_warnings = _config.ConfigItem(True, "Whether to log `warnings.warn` calls.")
log_exceptions = _config.ConfigItem(
False, "Whether to log exceptions before raising them."
)
log_to_file = _config.ConfigItem(
False, "Whether to always log messages to a log file."
)
log_file_path = _config.ConfigItem(
"",
"The file to log messages to. If empty string is given, "
"it defaults to a file ``'astropy.log'`` in "
"the astropy config directory.",
)
log_file_level = _config.ConfigItem(
"INFO", "Threshold for logging messages to `log_file_path`."
)
log_file_format = _config.ConfigItem(
"%(asctime)r, %(origin)r, %(levelname)r, %(message)r",
"Format for log file entries.",
)
log_file_encoding = _config.ConfigItem(
"",
"The encoding (e.g., UTF-8) to use for the log file. If empty string "
"is given, it defaults to the platform-preferred encoding.",
)
conf = Conf()
def _init_log():
"""Initializes the Astropy log--in most circumstances this is called
automatically when importing astropy.
"""
global log
orig_logger_cls = logging.getLoggerClass()
logging.setLoggerClass(AstropyLogger)
try:
log = logging.getLogger("astropy")
log._set_defaults()
finally:
logging.setLoggerClass(orig_logger_cls)
return log
def _teardown_log():
"""Shut down exception and warning logging (if enabled) and clear all
Astropy loggers from the logging module's cache.
This involves poking some logging module internals, so much if it is 'at
your own risk' and is allowed to pass silently if any exceptions occur.
"""
global log
if log.exception_logging_enabled():
log.disable_exception_logging()
if log.warnings_logging_enabled():
log.disable_warnings_logging()
del log
# Now for the fun stuff...
try:
logging._acquireLock()
try:
loggerDict = logging.Logger.manager.loggerDict
for key in loggerDict.keys():
if key == "astropy" or key.startswith("astropy."):
del loggerDict[key]
finally:
logging._releaseLock()
except Exception:
pass
Logger = logging.getLoggerClass()
class AstropyLogger(Logger):
"""
This class is used to set up the Astropy logging.
The main functionality added by this class over the built-in
logging.Logger class is the ability to keep track of the origin of the
messages, the ability to enable logging of warnings.warn calls and
exceptions, and the addition of colorized output and context managers to
easily capture messages to a file or list.
"""
def makeRecord(
self,
name,
level,
pathname,
lineno,
msg,
args,
exc_info,
func=None,
extra=None,
sinfo=None,
):
if extra is None:
extra = {}
if "origin" not in extra:
current_module = find_current_module(1, finddiff=[True, "logging"])
if current_module is not None:
extra["origin"] = current_module.__name__
else:
extra["origin"] = "unknown"
return Logger.makeRecord(
self,
name,
level,
pathname,
lineno,
msg,
args,
exc_info,
func=func,
extra=extra,
sinfo=sinfo,
)
_showwarning_orig = None
def _showwarning(self, *args, **kwargs):
# Bail out if we are not catching a warning from Astropy
if not isinstance(args[0], AstropyWarning):
return self._showwarning_orig(*args, **kwargs)
warning = args[0]
# Deliberately not using isinstance here: We want to display
# the class name only when it's not the default class,
# AstropyWarning. The name of subclasses of AstropyWarning should
# be displayed.
if type(warning) not in (AstropyWarning, AstropyUserWarning):
message = f"{warning.__class__.__name__}: {args[0]}"
else:
message = str(args[0])
mod_path = args[2]
# Now that we have the module's path, we look through sys.modules to
# find the module object and thus the fully-package-specified module
# name. The module.__file__ is the original source file name.
mod_name = None
mod_path = Path(mod_path).with_suffix("")
for mod in sys.modules.values():
try:
# Believe it or not this can fail in some cases:
# https://github.com/astropy/astropy/issues/2671
path = Path(getattr(mod, "__file__", "")).with_suffix("")
except Exception:
continue
if path == mod_path:
mod_name = mod.__name__
break
if mod_name is not None:
self.warning(message, extra={"origin": mod_name})
else:
self.warning(message)
def warnings_logging_enabled(self):
return self._showwarning_orig is not None
def enable_warnings_logging(self):
"""
Enable logging of warnings.warn() calls.
Once called, any subsequent calls to ``warnings.warn()`` are
redirected to this logger and emitted with level ``WARN``. Note that
this replaces the output from ``warnings.warn``.
This can be disabled with ``disable_warnings_logging``.
"""
if self.warnings_logging_enabled():
raise LoggingError("Warnings logging has already been enabled")
self._showwarning_orig = warnings.showwarning
warnings.showwarning = self._showwarning
def disable_warnings_logging(self):
"""
Disable logging of warnings.warn() calls.
Once called, any subsequent calls to ``warnings.warn()`` are no longer
redirected to this logger.
This can be re-enabled with ``enable_warnings_logging``.
"""
if not self.warnings_logging_enabled():
raise LoggingError("Warnings logging has not been enabled")
if warnings.showwarning != self._showwarning:
raise LoggingError(
"Cannot disable warnings logging: "
"warnings.showwarning was not set by this "
"logger, or has been overridden"
)
warnings.showwarning = self._showwarning_orig
self._showwarning_orig = None
_excepthook_orig = None
def _excepthook(self, etype, value, traceback):
if traceback is None:
mod = None
else:
tb = traceback
while tb.tb_next is not None:
tb = tb.tb_next
mod = inspect.getmodule(tb)
# include the error type in the message.
if len(value.args) > 0:
message = f"{etype.__name__}: {str(value)}"
else:
message = str(etype.__name__)
if mod is not None:
self.error(message, extra={"origin": mod.__name__})
else:
self.error(message)
self._excepthook_orig(etype, value, traceback)
def exception_logging_enabled(self):
"""
Determine if the exception-logging mechanism is enabled.
Returns
-------
exclog : bool
True if exception logging is on, False if not.
"""
if _WITHIN_IPYTHON:
from IPython import get_ipython
return _AstLogIPYExc in get_ipython().custom_exceptions
else:
return self._excepthook_orig is not None
def enable_exception_logging(self):
"""
Enable logging of exceptions.
Once called, any uncaught exceptions will be emitted with level
``ERROR`` by this logger, before being raised.
This can be disabled with ``disable_exception_logging``.
"""
if self.exception_logging_enabled():
raise LoggingError("Exception logging has already been enabled")
if _WITHIN_IPYTHON:
# IPython has its own way of dealing with excepthook
from IPython import get_ipython
# We need to locally define the function here, because IPython
# actually makes this a member function of their own class
def ipy_exc_handler(ipyshell, etype, evalue, tb, tb_offset=None):
# First use our excepthook
self._excepthook(etype, evalue, tb)
# Now also do IPython's traceback
ipyshell.showtraceback((etype, evalue, tb), tb_offset=tb_offset)
# now register the function with IPython
# note that we include _AstLogIPYExc so `disable_exception_logging`
# knows that it's disabling the right thing
get_ipython().set_custom_exc(
(BaseException, _AstLogIPYExc), ipy_exc_handler
)
# and set self._excepthook_orig to a no-op
self._excepthook_orig = lambda etype, evalue, tb: None
else:
# standard python interpreter
self._excepthook_orig = sys.excepthook
sys.excepthook = self._excepthook
def disable_exception_logging(self):
"""
Disable logging of exceptions.
Once called, any uncaught exceptions will no longer be emitted by this
logger.
This can be re-enabled with ``enable_exception_logging``.
"""
if not self.exception_logging_enabled():
raise LoggingError("Exception logging has not been enabled")
if _WITHIN_IPYTHON:
# IPython has its own way of dealing with exceptions
from IPython import get_ipython
get_ipython().set_custom_exc(tuple(), None)
else:
# standard python interpreter
if sys.excepthook != self._excepthook:
raise LoggingError(
"Cannot disable exception logging: "
"sys.excepthook was not set by this logger, "
"or has been overridden"
)
sys.excepthook = self._excepthook_orig
self._excepthook_orig = None
def enable_color(self):
"""
Enable colorized output.
"""
_conf.use_color = True
def disable_color(self):
"""
Disable colorized output.
"""
_conf.use_color = False
@contextmanager
def log_to_file(self, filename, filter_level=None, filter_origin=None):
"""
Context manager to temporarily log messages to a file.
Parameters
----------
filename : str
The file to log messages to.
filter_level : str
If set, any log messages less important than ``filter_level`` will
not be output to the file. Note that this is in addition to the
top-level filtering for the logger, so if the logger has level
'INFO', then setting ``filter_level`` to ``INFO`` or ``DEBUG``
will have no effect, since these messages are already filtered
out.
filter_origin : str
If set, only log messages with an origin starting with
``filter_origin`` will be output to the file.
Notes
-----
By default, the logger already outputs log messages to a file set in
the Astropy configuration file. Using this context manager does not
stop log messages from being output to that file, nor does it stop log
messages from being printed to standard output.
Examples
--------
The context manager is used as::
with logger.log_to_file('myfile.log'):
# your code here
"""
encoding = conf.log_file_encoding if conf.log_file_encoding else None
fh = logging.FileHandler(filename, encoding=encoding)
if filter_level is not None:
fh.setLevel(filter_level)
if filter_origin is not None:
fh.addFilter(FilterOrigin(filter_origin))
f = logging.Formatter(conf.log_file_format)
fh.setFormatter(f)
self.addHandler(fh)
yield
fh.close()
self.removeHandler(fh)
@contextmanager
def log_to_list(self, filter_level=None, filter_origin=None):
"""
Context manager to temporarily log messages to a list.
Parameters
----------
filename : str
The file to log messages to.
filter_level : str
If set, any log messages less important than ``filter_level`` will
not be output to the file. Note that this is in addition to the
top-level filtering for the logger, so if the logger has level
'INFO', then setting ``filter_level`` to ``INFO`` or ``DEBUG``
will have no effect, since these messages are already filtered
out.
filter_origin : str
If set, only log messages with an origin starting with
``filter_origin`` will be output to the file.
Notes
-----
Using this context manager does not stop log messages from being
output to standard output.
Examples
--------
The context manager is used as::
with logger.log_to_list() as log_list:
# your code here
"""
lh = ListHandler()
if filter_level is not None:
lh.setLevel(filter_level)
if filter_origin is not None:
lh.addFilter(FilterOrigin(filter_origin))
self.addHandler(lh)
yield lh.log_list
self.removeHandler(lh)
def _set_defaults(self):
"""
Reset logger to its initial state.
"""
# Reset any previously installed hooks
if self.warnings_logging_enabled():
self.disable_warnings_logging()
if self.exception_logging_enabled():
self.disable_exception_logging()
# Remove all previous handlers
for handler in self.handlers[:]:
self.removeHandler(handler)
# Set levels
self.setLevel(conf.log_level)
# Set up the stdout handler
sh = StreamHandler()
self.addHandler(sh)
# Set up the main log file handler if requested (but this might fail if
# configuration directory or log file is not writeable).
if conf.log_to_file:
log_file_path = conf.log_file_path
# "None" as a string because it comes from config
try:
_ASTROPY_TEST_ # noqa: B018
testing_mode = True
except NameError:
testing_mode = False
try:
if log_file_path == "" or testing_mode:
log_file_path = (
_config.get_config_dir_path("astropy") / "astropy.log"
)
else:
log_file_path = Path(log_file_path).expanduser()
encoding = conf.log_file_encoding if conf.log_file_encoding else None
fh = logging.FileHandler(log_file_path, encoding=encoding)
except OSError as e:
warnings.warn(
f"log file {log_file_path!r} could not be opened for writing:"
f" {str(e)}",
RuntimeWarning,
)
else:
formatter = logging.Formatter(conf.log_file_format)
fh.setFormatter(formatter)
fh.setLevel(conf.log_file_level)
self.addHandler(fh)
if conf.log_warnings:
self.enable_warnings_logging()
if conf.log_exceptions:
self.enable_exception_logging()
class StreamHandler(logging.StreamHandler):
"""
A specialized StreamHandler that logs INFO and DEBUG messages to
stdout, and all other messages to stderr. Also provides coloring
of the output, if enabled in the parent logger.
"""
def emit(self, record):
"""
The formatter for stderr.
"""
if record.levelno <= logging.INFO:
stream = sys.stdout
else:
stream = sys.stderr
if record.levelno < logging.DEBUG or not _conf.use_color:
print(record.levelname, end="", file=stream)
else:
# Import utils.console only if necessary and at the latest because
# the import takes a significant time [#4649]
from .utils.console import color_print
if record.levelno < logging.INFO:
color_print(record.levelname, "magenta", end="", file=stream)
elif record.levelno < logging.WARNING:
color_print(record.levelname, "green", end="", file=stream)
elif record.levelno < logging.ERROR:
color_print(record.levelname, "brown", end="", file=stream)
else:
color_print(record.levelname, "red", end="", file=stream)
# Make lazy interpretation intentional to leave the option to use
# special characters without escaping in log messages.
if record.args:
record.message = f"{record.msg % record.args} [{record.origin:s}]"
else:
record.message = f"{record.msg} [{record.origin:s}]"
print(": " + record.message, file=stream)
class FilterOrigin:
"""A filter for the record origin."""
def __init__(self, origin):
self.origin = origin
def filter(self, record):
return record.origin.startswith(self.origin)
class ListHandler(logging.Handler):
"""A handler that can be used to capture the records in a list."""
def __init__(self, filter_level=None, filter_origin=None):
logging.Handler.__init__(self)
self.log_list = []
def emit(self, record):
self.log_list.append(record)
|
astropyREPO_NAMEastropyPATH_START.@astropy_extracted@astropy-main@astropy@logger.py@.PATH_END.py
|
{
"filename": "_b0.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/contourcarpet/_b0.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class B0Validator(_plotly_utils.basevalidators.AnyValidator):
def __init__(self, plotly_name="b0", parent_name="contourcarpet", **kwargs):
super(B0Validator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc+clearAxisTypes"),
implied_edits=kwargs.pop("implied_edits", {"ytype": "scaled"}),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@contourcarpet@_b0.py@.PATH_END.py
|
{
"filename": "pannable_map.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/visualization/mapserver/pannable_map.py",
"type": "Python"
}
|
import os
from functools import wraps
import bottle
import numpy as np
from yt.fields.derived_field import ValidateSpatial
from yt.utilities.lib.misc_utilities import get_color_bounds
from yt.utilities.png_writer import write_png_to_string
from yt.visualization.fixed_resolution import FixedResolutionBuffer
from yt.visualization.image_writer import apply_colormap
local_dir = os.path.dirname(__file__)
def exc_writeout(f):
import traceback
@wraps(f)
def func(*args, **kwargs):
try:
rv = f(*args, **kwargs)
return rv
except Exception:
traceback.print_exc(None, open("temp.exc", "w"))
raise
return func
class PannableMapServer:
_widget_name = "pannable_map"
def __init__(self, data, field, takelog, cmap, route_prefix=""):
self.data = data
self.ds = data.ds
self.field = field
self.cmap = cmap
bottle.route(f"{route_prefix}/map/:field/:L/:x/:y.png")(self.map)
bottle.route(f"{route_prefix}/map/:field/:L/:x/:y.png")(self.map)
bottle.route(f"{route_prefix}/")(self.index)
bottle.route(f"{route_prefix}/:field")(self.index)
bottle.route(f"{route_prefix}/index.html")(self.index)
bottle.route(f"{route_prefix}/list", "GET")(self.list_fields)
# This is a double-check, since we do not always mandate this for
# slices:
self.data[self.field] = self.data[self.field].astype("float64", copy=False)
bottle.route(f"{route_prefix}/static/:path", "GET")(self.static)
self.takelog = takelog
self._lock = False
for unit in ["Gpc", "Mpc", "kpc", "pc"]:
v = self.ds.domain_width[0].in_units(unit).value
if v > 1:
break
self.unit = unit
self.px2unit = self.ds.domain_width[0].in_units(unit).value / 256
def lock(self):
import time
while self._lock:
time.sleep(0.01)
self._lock = True
def unlock(self):
self._lock = False
def map(self, field, L, x, y):
if "," in field:
field = tuple(field.split(","))
cmap = self.cmap
dd = 1.0 / (2.0 ** (int(L)))
relx = int(x) * dd
rely = int(y) * dd
DW = self.ds.domain_right_edge - self.ds.domain_left_edge
xl = self.ds.domain_left_edge[0] + relx * DW[0]
yl = self.ds.domain_left_edge[1] + rely * DW[1]
xr = xl + dd * DW[0]
yr = yl + dd * DW[1]
try:
self.lock()
w = 256 # pixels
data = self.data[field]
frb = FixedResolutionBuffer(self.data, (xl, xr, yl, yr), (w, w))
cmi, cma = get_color_bounds(
self.data["px"],
self.data["py"],
self.data["pdx"],
self.data["pdy"],
data,
self.ds.domain_left_edge[0],
self.ds.domain_right_edge[0],
self.ds.domain_left_edge[1],
self.ds.domain_right_edge[1],
dd * DW[0] / (64 * 256),
dd * DW[0],
)
finally:
self.unlock()
if self.takelog:
cmi = np.log10(cmi)
cma = np.log10(cma)
to_plot = apply_colormap(
np.log10(frb[field]), color_bounds=(cmi, cma), cmap_name=cmap
)
else:
to_plot = apply_colormap(
frb[field], color_bounds=(cmi, cma), cmap_name=cmap
)
rv = write_png_to_string(to_plot)
return rv
def index(self, field=None):
if field is not None:
self.field = field
return bottle.static_file(
"map_index.html", root=os.path.join(local_dir, "html")
)
def static(self, path):
if path[-4:].lower() in (".png", ".gif", ".jpg"):
bottle.response.headers["Content-Type"] = f"image/{path[-3:].lower()}"
elif path[-4:].lower() == ".css":
bottle.response.headers["Content-Type"] = "text/css"
elif path[-3:].lower() == ".js":
bottle.response.headers["Content-Type"] = "text/javascript"
full_path = os.path.join(os.path.join(local_dir, "html"), path)
return open(full_path).read()
def list_fields(self):
d = {}
# Add fluid fields (only gas for now)
for ftype in self.ds.fluid_types:
d[ftype] = []
for f in self.ds.derived_field_list:
if f[0] != ftype:
continue
# Discard fields which need ghost zones for now
df = self.ds.field_info[f]
if any(isinstance(v, ValidateSpatial) for v in df.validators):
continue
# Discard cutting plane fields
if "cutting" in f[1]:
continue
active = f[1] == self.field
d[ftype].append((f, active))
print(self.px2unit, self.unit)
return {
"data": d,
"px2unit": self.px2unit,
"unit": self.unit,
"active": self.field,
}
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@visualization@mapserver@pannable_map.py@.PATH_END.py
|
{
"filename": "_line.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/funnel/marker/_line.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Line(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "funnel.marker"
_path_str = "funnel.marker.line"
_valid_props = {
"autocolorscale",
"cauto",
"cmax",
"cmid",
"cmin",
"color",
"coloraxis",
"colorscale",
"colorsrc",
"reversescale",
"width",
"widthsrc",
}
# autocolorscale
# --------------
@property
def autocolorscale(self):
"""
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.line.colorscale`. Has an effect only if in
`marker.line.color`is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is true, the
default palette will be chosen according to whether numbers in
the `color` array are all positive, all negative or mixed.
The 'autocolorscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["autocolorscale"]
@autocolorscale.setter
def autocolorscale(self, val):
self["autocolorscale"] = val
# cauto
# -----
@property
def cauto(self):
"""
Determines whether or not the color domain is computed with
respect to the input data (here in `marker.line.color`) or the
bounds set in `marker.line.cmin` and `marker.line.cmax` Has an
effect only if in `marker.line.color`is set to a numerical
array. Defaults to `false` when `marker.line.cmin` and
`marker.line.cmax` are set by the user.
The 'cauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["cauto"]
@cauto.setter
def cauto(self, val):
self["cauto"] = val
# cmax
# ----
@property
def cmax(self):
"""
Sets the upper bound of the color domain. Has an effect only if
in `marker.line.color`is set to a numerical array. Value should
have the same units as in `marker.line.color` and if set,
`marker.line.cmin` must be set as well.
The 'cmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmax"]
@cmax.setter
def cmax(self, val):
self["cmax"] = val
# cmid
# ----
@property
def cmid(self):
"""
Sets the mid-point of the color domain by scaling
`marker.line.cmin` and/or `marker.line.cmax` to be equidistant
to this point. Has an effect only if in `marker.line.color`is
set to a numerical array. Value should have the same units as
in `marker.line.color`. Has no effect when `marker.line.cauto`
is `false`.
The 'cmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmid"]
@cmid.setter
def cmid(self, val):
self["cmid"] = val
# cmin
# ----
@property
def cmin(self):
"""
Sets the lower bound of the color domain. Has an effect only if
in `marker.line.color`is set to a numerical array. Value should
have the same units as in `marker.line.color` and if set,
`marker.line.cmax` must be set as well.
The 'cmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["cmin"]
@cmin.setter
def cmin(self, val):
self["cmin"] = val
# color
# -----
@property
def color(self):
"""
Sets themarker.linecolor. It accepts either a specific color or
an array of numbers that are mapped to the colorscale relative
to the max and min values of the array or relative to
`marker.line.cmin` and `marker.line.cmax` if set.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A number that will be interpreted as a color
according to funnel.marker.line.colorscale
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# coloraxis
# ---------
@property
def coloraxis(self):
"""
Sets a reference to a shared color axis. References to these
shared color axes are "coloraxis", "coloraxis2", "coloraxis3",
etc. Settings for these shared color axes are set in the
layout, under `layout.coloraxis`, `layout.coloraxis2`, etc.
Note that multiple color scales can be linked to the same color
axis.
The 'coloraxis' property is an identifier of a particular
subplot, of type 'coloraxis', that may be specified as the string 'coloraxis'
optionally followed by an integer >= 1
(e.g. 'coloraxis', 'coloraxis1', 'coloraxis2', 'coloraxis3', etc.)
Returns
-------
str
"""
return self["coloraxis"]
@coloraxis.setter
def coloraxis(self, val):
self["coloraxis"] = val
# colorscale
# ----------
@property
def colorscale(self):
"""
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array. The colorscale
must be an array containing arrays mapping a normalized value
to an rgb, rgba, hex, hsl, hsv, or named color string. At
minimum, a mapping for the lowest (0) and highest (1) values
are required. For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the colorscale in
color space, use`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette name string of the
following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Bl
ues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,Earth,Electric,Vi
ridis,Cividis.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of colors that will be spaced evenly to create the colorscale.
Many predefined colorscale lists are included in the sequential, diverging,
and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
'ylorrd'].
Appending '_r' to a named colorscale reverses it.
Returns
-------
str
"""
return self["colorscale"]
@colorscale.setter
def colorscale(self, val):
self["colorscale"] = val
# colorsrc
# --------
@property
def colorsrc(self):
"""
Sets the source reference on Chart Studio Cloud for color .
The 'colorsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["colorsrc"]
@colorsrc.setter
def colorsrc(self, val):
self["colorsrc"] = val
# reversescale
# ------------
@property
def reversescale(self):
"""
Reverses the color mapping if true. Has an effect only if in
`marker.line.color`is set to a numerical array. If true,
`marker.line.cmin` will correspond to the last color in the
array and `marker.line.cmax` will correspond to the first
color.
The 'reversescale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["reversescale"]
@reversescale.setter
def reversescale(self, val):
self["reversescale"] = val
# width
# -----
@property
def width(self):
"""
Sets the width (in px) of the lines bounding the marker points.
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
int|float|numpy.ndarray
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# widthsrc
# --------
@property
def widthsrc(self):
"""
Sets the source reference on Chart Studio Cloud for width .
The 'widthsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["widthsrc"]
@widthsrc.setter
def widthsrc(self, val):
self["widthsrc"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.line.colorscale`. Has an effect only if in
`marker.line.color`is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in
`marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has an
effect only if in `marker.line.color`is set to a
numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are set by
the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `marker.line.color`is set to a numerical
array. Value should have the same units as in
`marker.line.color` and if set, `marker.line.cmin` must
be set as well.
cmid
Sets the mid-point of the color domain by scaling
`marker.line.cmin` and/or `marker.line.cmax` to be
equidistant to this point. Has an effect only if in
`marker.line.color`is set to a numerical array. Value
should have the same units as in `marker.line.color`.
Has no effect when `marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `marker.line.color`is set to a numerical
array. Value should have the same units as in
`marker.line.color` and if set, `marker.line.cmax` must
be set as well.
color
Sets themarker.linecolor. It accepts either a specific
color or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `marker.line.cmin` and
`marker.line.cmax` if set.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette name
string of the following list: Greys,YlGnBu,Greens,YlOrR
d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H
ot,Blackbody,Earth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on Chart Studio Cloud for
color .
reversescale
Reverses the color mapping if true. Has an effect only
if in `marker.line.color`is set to a numerical array.
If true, `marker.line.cmin` will correspond to the last
color in the array and `marker.line.cmax` will
correspond to the first color.
width
Sets the width (in px) of the lines bounding the marker
points.
widthsrc
Sets the source reference on Chart Studio Cloud for
width .
"""
def __init__(
self,
arg=None,
autocolorscale=None,
cauto=None,
cmax=None,
cmid=None,
cmin=None,
color=None,
coloraxis=None,
colorscale=None,
colorsrc=None,
reversescale=None,
width=None,
widthsrc=None,
**kwargs
):
"""
Construct a new Line object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.funnel.marker.Line`
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`marker.line.colorscale`. Has an effect only if in
`marker.line.color`is set to a numerical array. In case
`colorscale` is unspecified or `autocolorscale` is
true, the default palette will be chosen according to
whether numbers in the `color` array are all positive,
all negative or mixed.
cauto
Determines whether or not the color domain is computed
with respect to the input data (here in
`marker.line.color`) or the bounds set in
`marker.line.cmin` and `marker.line.cmax` Has an
effect only if in `marker.line.color`is set to a
numerical array. Defaults to `false` when
`marker.line.cmin` and `marker.line.cmax` are set by
the user.
cmax
Sets the upper bound of the color domain. Has an effect
only if in `marker.line.color`is set to a numerical
array. Value should have the same units as in
`marker.line.color` and if set, `marker.line.cmin` must
be set as well.
cmid
Sets the mid-point of the color domain by scaling
`marker.line.cmin` and/or `marker.line.cmax` to be
equidistant to this point. Has an effect only if in
`marker.line.color`is set to a numerical array. Value
should have the same units as in `marker.line.color`.
Has no effect when `marker.line.cauto` is `false`.
cmin
Sets the lower bound of the color domain. Has an effect
only if in `marker.line.color`is set to a numerical
array. Value should have the same units as in
`marker.line.color` and if set, `marker.line.cmax` must
be set as well.
color
Sets themarker.linecolor. It accepts either a specific
color or an array of numbers that are mapped to the
colorscale relative to the max and min values of the
array or relative to `marker.line.cmin` and
`marker.line.cmax` if set.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorscale
Sets the colorscale. Has an effect only if in
`marker.line.color`is set to a numerical array. The
colorscale must be an array containing arrays mapping a
normalized value to an rgb, rgba, hex, hsl, hsv, or
named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For
example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`.
To control the bounds of the colorscale in color space,
use`marker.line.cmin` and `marker.line.cmax`.
Alternatively, `colorscale` may be a palette name
string of the following list: Greys,YlGnBu,Greens,YlOrR
d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H
ot,Blackbody,Earth,Electric,Viridis,Cividis.
colorsrc
Sets the source reference on Chart Studio Cloud for
color .
reversescale
Reverses the color mapping if true. Has an effect only
if in `marker.line.color`is set to a numerical array.
If true, `marker.line.cmin` will correspond to the last
color in the array and `marker.line.cmax` will
correspond to the first color.
width
Sets the width (in px) of the lines bounding the marker
points.
widthsrc
Sets the source reference on Chart Studio Cloud for
width .
Returns
-------
Line
"""
super(Line, self).__init__("line")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.funnel.marker.Line
constructor must be a dict or
an instance of :class:`plotly.graph_objs.funnel.marker.Line`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("autocolorscale", None)
_v = autocolorscale if autocolorscale is not None else _v
if _v is not None:
self["autocolorscale"] = _v
_v = arg.pop("cauto", None)
_v = cauto if cauto is not None else _v
if _v is not None:
self["cauto"] = _v
_v = arg.pop("cmax", None)
_v = cmax if cmax is not None else _v
if _v is not None:
self["cmax"] = _v
_v = arg.pop("cmid", None)
_v = cmid if cmid is not None else _v
if _v is not None:
self["cmid"] = _v
_v = arg.pop("cmin", None)
_v = cmin if cmin is not None else _v
if _v is not None:
self["cmin"] = _v
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("coloraxis", None)
_v = coloraxis if coloraxis is not None else _v
if _v is not None:
self["coloraxis"] = _v
_v = arg.pop("colorscale", None)
_v = colorscale if colorscale is not None else _v
if _v is not None:
self["colorscale"] = _v
_v = arg.pop("colorsrc", None)
_v = colorsrc if colorsrc is not None else _v
if _v is not None:
self["colorsrc"] = _v
_v = arg.pop("reversescale", None)
_v = reversescale if reversescale is not None else _v
if _v is not None:
self["reversescale"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
_v = arg.pop("widthsrc", None)
_v = widthsrc if widthsrc is not None else _v
if _v is not None:
self["widthsrc"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@funnel@marker@_line.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plazar/TOASTER",
"repo_path": "TOASTER_extracted/TOASTER-master/webtoaster/oauthclient/management/commands/__init__.py",
"type": "Python"
}
|
plazarREPO_NAMETOASTERPATH_START.@TOASTER_extracted@TOASTER-master@webtoaster@oauthclient@management@commands@__init__.py@.PATH_END.py
|
|
{
"filename": "clusters_z6_study.py",
"repo_name": "ICRAR/shark",
"repo_path": "shark_extracted/shark-master/standard_plots/clusters_z6_study.py",
"type": "Python"
}
|
#
# ICRAR - International Centre for Radio Astronomy Research
# (c) UWA - The University of Western Australia, 2018
# Copyright by UWA (in the framework of the ICRAR)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
import functools
import numpy as np
import h5py
import common
import utilities_statistics as us
##################################
# Constants
mlow = 7.0
mupp = 13.0
dm = 0.5
mbins = np.arange(mlow, mupp, dm)
xmf = mbins + dm/2.0
#rbins = np.array([1, 2.730045591676135, 5]) #Mpc/h
rbins = np.array([1, 2.8315841879187973, 5]) #Mpc/h
zdepth = 40.30959350543804 #Mpc/h
GyrtoYr = 1e9
MpcToKpc = 1e3
G = 4.299e-9 #Gravity constant in units of (km/s)^2 * Mpc/Msun
PI = 3.1416
def add_observations_to_plot(obsdir, fname, ax, marker, label, color='k', err_absolute=False):
fname = '%s/Gas/%s' % (obsdir, fname)
x, y, yerr_down, yerr_up = common.load_observation(obsdir, fname, (0, 1, 2, 3))
common.errorbars(ax, x, y, yerr_down, yerr_up, color, marker, label=label, err_absolute=err_absolute)
def prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit):
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit)
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
#ax.text(xleg, yleg, 'z=0')
def prepare_data(hdf5_data, seds_ap, seds_ab, index, zlist):
# Unpack data
(h0, _, typeg, mdisk, mbulge, _, _, mHI, mH2, mgas,
mHI_bulge, mH2_bulge, mgas_bulge, mvir, sfrd, sfrb,
x, y, z, vvir, vx, vy, vz, id_halo_tree) = hdf5_data
ap_mags = seds_ap[0]
ab_mags = seds_ab[0]
ind = np.where( (mdisk + mbulge) > 0)
mvir = mvir[ind]
vvir = vvir[ind]
mH2 = mH2[ind]
mH2_bulge = mH2_bulge[ind]
x = x[ind]
y = y[ind]
z = z[ind]
vx = vx[ind]
vy = vy[ind]
vz = vz[ind]
mdisk = mdisk[ind]
mbulge = mbulge[ind]
sfrd = sfrd[ind]
sfrb = sfrb[ind]
typeg = typeg[ind]
id_halo_tree = id_halo_tree[ind]
XH = 0.72
h0log = np.log10(float(h0))
rvir = G * mvir / pow(vvir,2.0) / h0
mstar_tot = (mdisk + mbulge) / h0
sfr_tot = (sfrd + sfrb) / h0 / GyrtoYr
#find most massive halos
indcen = np.where((mvir/h0 > 1e11) & (typeg == 0))
mvir_in = mvir[indcen]/h0
sortedmass = np.argsort(1.0/mvir_in)
mass_ordered = mvir_in[sortedmass]
mass_tresh = mass_ordered[9]
print(max(mvir/h0), mass_ordered)
indcen = np.where((mvir/h0 >= mass_tresh) & (typeg == 0))
print(mvir[indcen]/h0)
x_cen = x[indcen]
y_cen = y[indcen]
z_cen = z[indcen]
vx_cen = vx[indcen]
vy_cen = vy[indcen]
vz_cen = vz[indcen]
mvir_cen = mvir[indcen]/h0
rvir_cen = rvir[indcen]
#Bands information:
#(0): "FUV_GALEX", "NUV_GALEX", "u_SDSS", "g_SDSS", "r_SDSS", "i_SDSS",
#(6): "z_SDSS", "Y_VISTA", "J_VISTA", "H_VISTA", "K_VISTA", "W1_WISE",
#(12): "I1_Spitzer", "I2_Spitzer", "W2_WISE", "I3_Spitzer", "I4_Spitzer",
#(17): "W3_WISE", "W4_WISE", "P70_Herschel", "P100_Herschel",
#(21): "P160_Herschel", "S250_Herschel", "S350_Herschel", "S450_JCMT",
#(25): "S500_Herschel", "S850_JCMT", "Band9_ALMA", "Band8_ALMA",
#(29): "Band7_ALMA", "Band6_ALMA", "Band5_ALMA", "Band4_ALMA"
writeon = True
bands = range(0,11)
#xyz distance
for g in range(0,len(x_cen)):
d_all = np.sqrt(pow(x - x_cen[g], 2.0) + pow(y - y_cen[g], 2.0) + pow(z - z_cen[g], 2.0))
ind = np.where((d_all < 2))
massin = mstar_tot[ind]
sfrin = sfr_tot[ind]
xin = x[ind] - x_cen[g]
yin = y[ind] - y_cen[g]
zin = z[ind] - z_cen[g]
vxin = vx[ind] - vx_cen[g]
vyin = vy[ind] - vy_cen[g]
vzin = vz[ind] - vz_cen[g]
typegin = typeg[ind]
id_halo_treein = id_halo_tree[ind]
apssed = ap_mags[:,ind]
abssed = ab_mags[:,ind]
apssed = apssed[:,0,:]
abssed = abssed[:,0,:]
print(apssed.shape)
if(writeon):
f = open('cluster_z' + str(zlist[index]) + "_" + str(g) + '.txt', 'w')
f.write("Mvir[Msun]: %s\n" % (str(mvir_cen[g])))
f.write("#central galaxy has positions=0 and velocities=0\n")
f.write("#all galaxies within a sphere of radius 2 comoving Mpc included\n")
f.write("#mstar[Msun] sfr[Msun/yr] x[cMpc] y[cMpc] z[cMpc] vx[km/s] vy[km/s] vz[km/s] typeg(=0 centrals) id_halo_tree ap_mag[AB] (FUV_GALEX, NUV_GALEXi, u_SDSS, g_SDSS, r_SDSS, i_SDSS, z_SDSS, Y_VISTA, J_VISTA, H_VISTA, K_VISTA) ab_mag[AB] (FUV_GALEX, NUV_GALEXi, u_SDSS, g_SDSS, r_SDSS, i_SDSS, z_SDSS, Y_VISTA, J_VISTA, H_VISTA, K_VISTA)\n")
for i in range(0,len(massin)):
srt_to_write = str(massin[i]) + ' ' + str(sfrin[i]) + ' ' + str(xin[i]) + ' ' + str(yin[i]) +' ' + str(zin[i]) +' ' + str(vxin[i]) +' ' + str(vyin[i]) +' ' + str(vzin[i]) + ' ' +str(typegin[i]) + ' ' + str(id_halo_treein[i])
for b in bands:
srt_to_write += ' ' + str(apssed[b,i])
for b in bands:
srt_to_write += ' ' + str(abssed[b,i])
srt_to_write += "\n"
f.write(srt_to_write)
f.close()
def main(model_dir, output_dir, redshift_table, subvols, obs_dir):
plt = common.load_matplotlib()
zlist = (5.0, 6.0)
fields = {'galaxies': ('type', 'mstars_disk', 'mstars_bulge',
'rstar_disk', 'm_bh', 'matom_disk', 'mmol_disk', 'mgas_disk',
'matom_bulge', 'mmol_bulge', 'mgas_bulge', 'mvir_hosthalo', 'sfr_disk',
'sfr_burst', 'position_x', 'position_y', 'position_z', 'vvir_hosthalo',
'velocity_x', 'velocity_y', 'velocity_z', 'id_halo_tree')}
file_hdf5_sed = "Shark-SED-eagle-rr14.hdf5"
fields_sed_ap = {'SED/ap_dust': ('total','disk'),}
fields_sed_ab = {'SED/ab_dust': ('total','disk'),}
for index, snapshot in enumerate(redshift_table[zlist]):
hdf5_data = common.read_data(model_dir, snapshot, fields, subvols)
seds_ap = common.read_photometry_data_variable_tau_screen(model_dir, snapshot, fields_sed_ap, subvols, file_hdf5_sed)
seds_ab = common.read_photometry_data_variable_tau_screen(model_dir, snapshot, fields_sed_ab, subvols, file_hdf5_sed)
prepare_data(hdf5_data, seds_ap, seds_ab, index, zlist)
if __name__ == '__main__':
main(*common.parse_args())
|
ICRARREPO_NAMEsharkPATH_START.@shark_extracted@shark-master@standard_plots@clusters_z6_study.py@.PATH_END.py
|
{
"filename": "calc_tau_gas.py",
"repo_name": "Jingxuan97/nemesispy",
"repo_path": "nemesispy_extracted/nemesispy-main/nemesispy/radtran/calc_tau_gas.py",
"type": "Python"
}
|
#!/usr/local/bin/python3
# -*- coding: utf-8 -*-
"""
Calculate the optical path due to atomic and molecular lines.
The opacity of gases is calculated by the correlated-k method
using pre-tabulated ktables, assuming random overlap of lines.
"""
import numpy as np
from numba import jit
@jit(nopython=True)
def calc_tau_gas(k_gas_w_g_p_t, P_layer, T_layer, VMR_layer, U_layer,
P_grid, T_grid, del_g):
"""
Parameters
----------
k_gas_w_g_p_t(NGAS,NWAVE,NG,NPRESSK,NTEMPK) : ndarray
k-coefficients.
Unit: cm^2 (per particle)
Has dimension: NGAS x NWAVE x NG x NPRESSK x NTEMPK.
P_layer(NLAYER) : ndarray
Atmospheric pressure grid.
Unit: Pa
T_layer(NLAYER) : ndarray
Atmospheric temperature grid.
Unit: K
VMR_layer(NLAYER,NGAS) : ndarray
Array of volume mixing ratios for NGAS.
Has dimensioin: NLAYER x NGAS
U_layer(NLAYER) : ndarray
Total number of gas particles in each layer.
Unit: (no. of particle) m^-2
P_grid(NPRESSK) : ndarray
Pressure grid on which the k-coeff's are pre-computed.
T_grid(NTEMPK) : ndarray
Temperature grid on which the k-coeffs are pre-computed.
del_g(NG) : ndarray
Gauss quadrature weights for the g-ordinates.
These are the widths of the bins in g-space.
n_active : int
Number of spectrally active gases.
Returns
-------
tau_w_g_l(NWAVE,NG,NLAYER) : ndarray
Optical path due to spectral line absorptions.
Notes
-----
Absorber amounts (U_layer) is scaled down by a factor 1e-20 because Nemesis
k-tables are scaled up by a factor of 1e20.
"""
# convert from per m^2 to per cm^2 and downscale Nemesis opacity by 1e-20
Scaled_U_layer = U_layer * 1.0e-20 * 1.0e-4
Ngas, Nwave, Ng = k_gas_w_g_p_t.shape[0:3]
Nlayer = len(P_layer)
tau_w_g_l = np.zeros((Nwave,Ng,Nlayer))
# if only has 1 active gas, skip random overlap
if Ngas == 1:
k_w_g_l = interp_k(P_grid, T_grid, P_layer, T_layer,
k_gas_w_g_p_t[0,:,:,:])
for ilayer in range(Nlayer):
tau_w_g_l[:,:,ilayer] = k_w_g_l[:,:,ilayer] \
* Scaled_U_layer[ilayer] * VMR_layer[ilayer,0]
# if there are multiple gases, combine their opacities
else:
k_gas_w_g_l = np.zeros((Ngas,Nwave,Ng,Nlayer))
for igas in range(Ngas):
k_gas_w_g_l[igas,:,:,:,] \
= interp_k(P_grid, T_grid, P_layer, T_layer,
k_gas_w_g_p_t[igas,:,:,:,:])
for iwave in range (Nwave):
for ilayer in range(Nlayer):
amount_layer = Scaled_U_layer[ilayer] * VMR_layer[ilayer,:Ngas]
tau_w_g_l[iwave,:,ilayer]\
= noverlapg(k_gas_w_g_l[:,iwave,:,ilayer],
amount_layer,del_g)
return tau_w_g_l
@jit(nopython=True)
def interp_k(P_grid, T_grid, P_layer, T_layer, k_w_g_p_t):
"""
Interpolate the k coeffcients at given atmospheric presures and temperatures
using a k-table.
Parameters
----------
P_grid(NPRESSKTA) : ndarray
Pressure grid of the k-tables.
Unit: Pa
T_grid(NTEMPKTA) : ndarray
Temperature grid of the ktables.
Unit: Kelvin
P_layer(NLAYER) : ndarray
Atmospheric pressure grid.
Unit: Pa
T_layer(NLAYER) : ndarray
Atmospheric temperature grid.
Unit: Kelvin
k_w_g_p_t(NGAS,NWAVEKTA,NG,NPRESSKTA,NTEMPKTA) : ndarray
Array storing the k-coefficients.
Returns
-------
k_w_g_l(NGAS,NWAVEKTA,NG,NLAYER) : ndarray
The interpolated-to-atmosphere k-coefficients.
Has dimension: NWAVE x NG x NLAYER.
Notes
-----
Code breaks if P_layer/T_layer is out of the range of P_grid/T_grid.
Mainly need to worry about max(T_layer)>max(T_grid).
No extrapolation outside of the TP grid of ktable.
"""
NWAVE, NG, NPRESS, NTEMP = k_w_g_p_t.shape
NLAYER = len(P_layer)
k_w_g_l = np.zeros((NWAVE,NG,NLAYER))
# Interpolate the k values at the layer temperature and pressure
for ilayer in range(NLAYER):
p = P_layer[ilayer]
t = T_layer[ilayer]
# Find pressure grid points above and below current layer pressure
ip = np.abs(P_grid-p).argmin()
if P_grid[ip] >= p:
ip_high = ip
if ip == 0:
p = P_grid[0]
ip_low = 0
ip_high = 1
else:
ip_low = ip-1
elif P_grid[ip]<p:
ip_low = ip
if ip == NPRESS-1:
p = P_grid[NPRESS-1]
ip_high = NPRESS-1
ip_low = NPRESS-2
else:
ip_high = ip + 1
# Find temperature grid points above and below current layer temperature
it = np.abs(T_grid-t).argmin()
if T_grid[it] >= t:
it_high = it
if it == 0:
t = T_grid[0]
it_low = 0
it_high = 1
else:
it_low = it -1
elif T_grid[it] < t:
it_low = it
if it == NTEMP-1:
t = T_grid[-1]
it_high = NTEMP - 1
it_low = NTEMP -2
else:
it_high = it + 1
### test
# Set up arrays for interpolation
# this worked
lnp = np.log(p)
lnp_low = np.log(P_grid[ip_low])
lnp_high = np.log(P_grid[ip_high])
# # testing
# lnp = (p)
# lnp_low = P_grid[ip_low]
# lnp_high = P_grid[ip_high]
# #
t_low = T_grid[it_low]
t_high = T_grid[it_high]
# NGAS,NWAVE,NG
f11 = k_w_g_p_t[:,:,ip_low,it_low]
f12 = k_w_g_p_t[:,:,ip_low,it_high]
f21 = k_w_g_p_t[:,:,ip_high,it_high]
f22 = k_w_g_p_t[:,:,ip_high,it_low]
# Bilinear interpolation
v = (lnp-lnp_low)/(lnp_high-lnp_low)
u = (t-t_low)/(t_high-t_low)
igood = np.where( (f11>0.0) & (f12>0.0) & (f22>0.0) & (f21>0.0) )
ibad = np.where( (f11<=0.0) & (f12<=0.0) & (f22<=0.0) & (f21<=0.0) )
for i in range(len(igood[0])):
# this worked
k_w_g_l[igood[0][i],igood[1][i],ilayer] \
= (1.0-v)*(1.0-u)*np.log(f11[igood[0][i],igood[1][i]]) \
+ v*(1.0-u)*np.log(f22[igood[0][i],igood[1][i]]) \
+ v*u*np.log(f21[igood[0][i],igood[1][i]]) \
+ (1.0-v)*u*np.log(f12[igood[0][i],igood[1][i]])
k_w_g_l[igood[0][i],igood[1][i],ilayer] \
= np.exp(k_w_g_l[igood[0][i],igood[1][i],ilayer])
# # testing
# k_w_g_l[igood[0][i],igood[1][i],ilayer] \
# = (1.0-v)*(1.0-u)*(f11[igood[0][i],igood[1][i]]) \
# + v*(1.0-u)*(f22[igood[0][i],igood[1][i]]) \
# + v*u*(f21[igood[0][i],igood[1][i]]) \
# + (1.0-v)*u*(f12[igood[0][i],igood[1][i]])
# #
for i in range(len(ibad[0])):
k_w_g_l[ibad[0][i],ibad[1][i],ilayer] \
= (1.0-v)*(1.0-u)*f11[ibad[0][i],ibad[1][i]] \
+ v*(1.0-u)*f22[ibad[0][i],ibad[1][i]] \
+ v*u*f21[ibad[0][i],ibad[1][i]] \
+ (1.0-v)*u*f12[ibad[0][i],ibad[1][i]]
return k_w_g_l
@jit(nopython=True)
def rank(weight, cont, del_g):
"""
Combine the randomly overlapped k distributions of two gases
into a single k distribution.
Parameters
----------
weight(NG) : ndarray
Weights of points in the random k-dist
cont(NG) : ndarray
Random k-coeffs in the k-dist.
del_g(NG) : ndarray
Required weights of final k-dist.
Returns
-------
k_g(NG) : ndarray
Combined k-dist.
Unit: cm^2 (per particle)
"""
ng = len(del_g)
nloop = ng*ng
# sum delta gs to get cumulative g ordinate
g_ord = np.zeros(ng+1)
g_ord[1:] = np.cumsum(del_g)
g_ord[ng] = 1
# Sort random k-coeffs into ascending order. Integer array ico records
# which swaps have been made so that we can also re-order the weights.
ico = np.argsort(cont)
cont = cont[ico]
weight = weight[ico] # sort weights accordingly
gdist = np.cumsum(weight)
k_g = np.zeros(ng)
ig = 0
sum1 = 0.0
cont_weight = cont * weight
for iloop in range(nloop):
if gdist[iloop] < g_ord[ig+1]:
k_g[ig] = k_g[ig] + cont_weight[iloop]
sum1 = sum1 + weight[iloop]
else:
frac = (g_ord[ig+1] - gdist[iloop-1])/(gdist[iloop]-gdist[iloop-1])
k_g[ig] = k_g[ig] + np.float32(frac)*cont_weight[iloop]
sum1 = sum1 + frac * weight[iloop]
k_g[ig] = k_g[ig]/np.float32(sum1)
ig = ig +1
sum1 = (1.0-frac)*weight[iloop]
k_g[ig] = np.float32(1.0-frac)*cont_weight[iloop]
if ig == ng-1:
k_g[ig] = k_g[ig]/np.float32(sum1)
return k_g
@jit(nopython=True)
def noverlapg(k_gas_g, amount, del_g):
"""
Combine k distributions of multiple gases given their number densities.
Parameters
----------
k_gas_g(NGAS,NG) : ndarray
K-distributions of the different gases.
Each row contains a k-distribution defined at NG g-ordinates.
Unit: cm^2 (per particle)
amount(NGAS) : ndarray
Absorber amount of each gas,
i.e. amount = VMR x layer absorber per area
Unit: (no. of partiicles) cm^-2
del_g(NG) : ndarray
Gauss quadrature weights for the g-ordinates.
These are the widths of the bins in g-space.
Returns
-------
tau_g(NG) : ndarray
Opatical path from mixing k-distribution weighted by absorber amounts.
Unit: dimensionless
"""
NGAS = len(amount)
NG = len(del_g)
tau_g = np.zeros(NG)
random_weight = np.zeros(NG*NG)
random_tau = np.zeros(NG*NG)
cutoff = 1e-12
for igas in range(NGAS-1):
# first pair of gases
if igas == 0:
# if opacity due to first gas is negligible
if k_gas_g[igas,:][-1] * amount[igas] < cutoff:
tau_g = k_gas_g[igas+1,:] * amount[igas+1]
# if opacity due to second gas is negligible
elif k_gas_g[igas+1,:][-1] * amount[igas+1] < cutoff:
tau_g = k_gas_g[igas,:] * amount[igas]
# else resort-rebin with random overlap approximation
else:
iloop = 0
for ig in range(NG):
for jg in range(NG):
random_weight[iloop] = del_g[ig] * del_g[jg]
random_tau[iloop] = k_gas_g[igas,:][ig] * amount[igas] \
+ k_gas_g[igas+1,:][jg] * amount[igas+1]
iloop = iloop + 1
tau_g = rank(random_weight,random_tau,del_g)
# subsequent gases, add amount*k to previous summed k
else:
# if opacity due to next gas is negligible
if k_gas_g[igas+1,:][-1] * amount[igas+1] < cutoff:
pass
# if opacity due to previous gases is negligible
elif tau_g[-1] < cutoff:
tau_g = k_gas_g[igas+1,:] * amount[igas+1]
# else resort-rebin with random overlap approximation
else:
iloop = 0
for ig in range(NG):
for jg in range(NG):
random_weight[iloop] = del_g[ig] * del_g[jg]
random_tau[iloop] = tau_g[ig] \
+ k_gas_g[igas+1,:][jg] * amount[igas+1]
iloop = iloop + 1
tau_g = rank(random_weight,random_tau,del_g)
return tau_g
|
Jingxuan97REPO_NAMEnemesispyPATH_START.@nemesispy_extracted@nemesispy-main@nemesispy@radtran@calc_tau_gas.py@.PATH_END.py
|
{
"filename": "common.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/pyzmq/py2/zmq/eventloop/minitornado/platform/common.py",
"type": "Python"
}
|
"""Lowest-common-denominator implementations of platform functionality."""
from __future__ import absolute_import, division, print_function, with_statement
import errno
import socket
from . import interface
class Waker(interface.Waker):
"""Create an OS independent asynchronous pipe.
For use on platforms that don't have os.pipe() (or where pipes cannot
be passed to select()), but do have sockets. This includes Windows
and Jython.
"""
def __init__(self):
# Based on Zope async.py: http://svn.zope.org/zc.ngi/trunk/src/zc/ngi/async.py
self.writer = socket.socket()
# Disable buffering -- pulling the trigger sends 1 byte,
# and we want that sent immediately, to wake up ASAP.
self.writer.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
count = 0
while 1:
count += 1
# Bind to a local port; for efficiency, let the OS pick
# a free port for us.
# Unfortunately, stress tests showed that we may not
# be able to connect to that port ("Address already in
# use") despite that the OS picked it. This appears
# to be a race bug in the Windows socket implementation.
# So we loop until a connect() succeeds (almost always
# on the first try). See the long thread at
# http://mail.zope.org/pipermail/zope/2005-July/160433.html
# for hideous details.
a = socket.socket()
a.bind(("127.0.0.1", 0))
a.listen(1)
connect_address = a.getsockname() # assigned (host, port) pair
try:
self.writer.connect(connect_address)
break # success
except socket.error as detail:
if (not hasattr(errno, 'WSAEADDRINUSE') or
detail[0] != errno.WSAEADDRINUSE):
# "Address already in use" is the only error
# I've seen on two WinXP Pro SP2 boxes, under
# Pythons 2.3.5 and 2.4.1.
raise
# (10048, 'Address already in use')
# assert count <= 2 # never triggered in Tim's tests
if count >= 10: # I've never seen it go above 2
a.close()
self.writer.close()
raise socket.error("Cannot bind trigger!")
# Close `a` and try again. Note: I originally put a short
# sleep() here, but it didn't appear to help or hurt.
a.close()
self.reader, addr = a.accept()
self.reader.setblocking(0)
self.writer.setblocking(0)
a.close()
self.reader_fd = self.reader.fileno()
def fileno(self):
return self.reader.fileno()
def write_fileno(self):
return self.writer.fileno()
def wake(self):
try:
self.writer.send(b"x")
except (IOError, socket.error):
pass
def consume(self):
try:
while True:
result = self.reader.recv(1024)
if not result:
break
except (IOError, socket.error):
pass
def close(self):
self.reader.close()
self.writer.close()
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@pyzmq@py2@zmq@eventloop@minitornado@platform@common.py@.PATH_END.py
|
{
"filename": "_font.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/hoverlabel/_font.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FontValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="font", parent_name="layout.hoverlabel", **kwargs):
super(FontValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Font"),
data_docs=kwargs.pop(
"data_docs",
"""
color
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans", "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text,
such as an "under", "over" or "through" as well
as combinations e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind
text. "auto" places minimal shadow and applies
contrast text font color. See
https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional
options.
size
style
Sets whether a font should be styled with a
normal or italic face from its family.
textcase
Sets capitalization of text. It can be used to
make text appear in all-uppercase or all-
lowercase, or with each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
""",
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@hoverlabel@_font.py@.PATH_END.py
|
{
"filename": "_showticksuffix.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/surface/colorbar/_showticksuffix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowticksuffixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="showticksuffix", parent_name="surface.colorbar", **kwargs
):
super(ShowticksuffixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@surface@colorbar@_showticksuffix.py@.PATH_END.py
|
{
"filename": "root_handler.py",
"repo_name": "threeML/hawc_hal",
"repo_path": "hawc_hal_extracted/hawc_hal-master/hawc_hal/root_handler.py",
"type": "Python"
}
|
# This module handle the importing of ROOT
# only use in methods that actually need ROOT
# NOTE:Moving to uproot to remove all ROOT dependencies given that support
# for root-numpy has stopped
# import ROOT
# ROOT.PyConfig.IgnoreCommandLineOptions = True
# from ROOT import TEntryList
# from threeML.io.cern_root_utils.io_utils import get_list_of_keys, open_ROOT_file
# from threeML.io.cern_root_utils.tobject_to_numpy import tree_to_ndarray
# ROOT.SetMemoryPolicy(ROOT.kMemoryStrict)
# import root_numpy
|
threeMLREPO_NAMEhawc_halPATH_START.@hawc_hal_extracted@hawc_hal-master@hawc_hal@root_handler.py@.PATH_END.py
|
{
"filename": "test_version.py",
"repo_name": "transientskp/tkp",
"repo_path": "tkp_extracted/tkp-master/tests/test_database/test_version.py",
"type": "Python"
}
|
import unittest
from tkp.db.model import Version, SCHEMA_VERSION
from tkp.db.database import Database
from tkp.testutil.decorators import database_disabled
class TestVersion(unittest.TestCase):
def setUp(self):
# Can't use a regular skip here, due to a Nose bug:
# https://github.com/nose-devs/nose/issues/946
if database_disabled():
raise unittest.SkipTest("Database functionality disabled "
"in configuration.")
self.database = Database()
self.database.connect()
def test_version(self):
session = self.database.Session()
v = session.query(Version).filter(Version.name == 'revision').one()
self.assertEqual(v.value, SCHEMA_VERSION)
|
transientskpREPO_NAMEtkpPATH_START.@tkp_extracted@tkp-master@tests@test_database@test_version.py@.PATH_END.py
|
{
"filename": "particle_xy_plot.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/doc/source/cookbook/particle_xy_plot.py",
"type": "Python"
}
|
import yt
# load the dataset
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
# create our plot
p = yt.ParticlePlot(
ds,
("all", "particle_position_x"),
("all", "particle_position_y"),
("all", "particle_mass"),
width=(0.5, 0.5),
)
# pick some appropriate units
p.set_axes_unit("kpc")
p.set_unit(("all", "particle_mass"), "Msun")
# save result
p.save()
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@doc@source@cookbook@particle_xy_plot.py@.PATH_END.py
|
{
"filename": "test_lookfor.py",
"repo_name": "scikit-image/scikit-image",
"repo_path": "scikit-image_extracted/scikit-image-main/skimage/util/tests/test_lookfor.py",
"type": "Python"
}
|
import skimage as ski
def test_lookfor_basic(capsys):
assert ski.lookfor is ski.util.lookfor
ski.util.lookfor("regionprops")
search_results = capsys.readouterr().out
assert "skimage.measure.regionprops" in search_results
assert "skimage.measure.regionprops_table" in search_results
|
scikit-imageREPO_NAMEscikit-imagePATH_START.@scikit-image_extracted@scikit-image-main@skimage@util@tests@test_lookfor.py@.PATH_END.py
|
{
"filename": "test_aggregator.py",
"repo_name": "rhayes777/PyAutoFit",
"repo_path": "PyAutoFit_extracted/PyAutoFit-main/test_autofit/aggregator/test_aggregator.py",
"type": "Python"
}
|
def test_completed_aggregator(
aggregator
):
aggregator = aggregator(
aggregator.search.is_complete
)
assert len(aggregator) == 1
class TestLoading:
def test_unzip(self, aggregator):
assert len(aggregator) == 2
def test_pickles(self, aggregator):
assert list(aggregator.values("dataset"))[0]["name"] == "dataset"
class TestOperations:
def test_attribute(self, aggregator):
assert list(aggregator.values("pipeline")) == [
"pipeline0",
"pipeline1"
]
def test_indexing(self, aggregator):
assert list(aggregator[1:].values("pipeline")) == ["pipeline1"]
assert list(aggregator[:1].values("pipeline")) == ["pipeline0"]
assert list(aggregator[1: 2].values("pipeline")) == ["pipeline1"]
assert list(aggregator[0: 1].values("pipeline")) == ["pipeline0"]
assert list(aggregator[-1:].values("pipeline")) == ["pipeline1"]
assert list(aggregator[:-1].values("pipeline")) == ["pipeline0"]
assert aggregator[0]["pipeline"] == "pipeline0"
assert aggregator[-1]["pipeline"] == "pipeline1"
def test_map(self, aggregator):
def some_function(fit):
return f"{fit.id} {fit['pipeline']}"
results = aggregator.map(some_function)
assert list(results) == [
'complete pipeline0',
'incomplete pipeline1'
]
|
rhayes777REPO_NAMEPyAutoFitPATH_START.@PyAutoFit_extracted@PyAutoFit-main@test_autofit@aggregator@test_aggregator.py@.PATH_END.py
|
{
"filename": "_weight.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/scene/yaxis/title/font/_weight.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class WeightValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self,
plotly_name="weight",
parent_name="layout.scene.yaxis.title.font",
**kwargs,
):
super(WeightValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
extras=kwargs.pop("extras", ["normal", "bold"]),
max=kwargs.pop("max", 1000),
min=kwargs.pop("min", 1),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@scene@yaxis@title@font@_weight.py@.PATH_END.py
|
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