id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
12,000 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _image_to_data | def _image_to_data(img):
"""
Does the work of encoding an image into Base64
"""
# If the image is already encoded in Base64, we have nothing to do here
if "src" not in img.attrs or img["src"].startswith("data:"):
return
elif re.match("https?://", img["src"]):
img_data = _load_url(img["src"]).read()
else:
img_data = _load_file(img["src"]).read()
img_type = imghdr.what("", img_data)
img_b64 = base64.b64encode(img_data)
src_data = "data:image/none;base64,"
if img_type:
src_data = "data:image/{};base64,{}".format(img_type, img_b64)
img["src"] = src_data | python | def _image_to_data(img):
# If the image is already encoded in Base64, we have nothing to do here
if "src" not in img.attrs or img["src"].startswith("data:"):
return
elif re.match("https?://", img["src"]):
img_data = _load_url(img["src"]).read()
else:
img_data = _load_file(img["src"]).read()
img_type = imghdr.what("", img_data)
img_b64 = base64.b64encode(img_data)
src_data = "data:image/none;base64,"
if img_type:
src_data = "data:image/{};base64,{}".format(img_type, img_b64)
img["src"] = src_data | [
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12,001 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _bake_css | def _bake_css(link):
"""
Takes a link element and turns it into an inline style link if applicable
"""
if "href" in link.attrs and (re.search("\.css$", link["href"])) or ("rel" in link.attrs and link["rel"] is "stylesheet") or ("type" in link.attrs and link["type"] is "text/css"):
if re.match("https?://", link["href"]):
css_data = _load_url(link["href"]).read()
else:
css_data = _load_file(link["href"]).read()
link.clear()
if USING_PYTHON2:
link.string = css_data
else:
link.string = str(css_data)
link.name = "style"
del link["rel"]
del link["href"] | python | def _bake_css(link):
if "href" in link.attrs and (re.search("\.css$", link["href"])) or ("rel" in link.attrs and link["rel"] is "stylesheet") or ("type" in link.attrs and link["type"] is "text/css"):
if re.match("https?://", link["href"]):
css_data = _load_url(link["href"]).read()
else:
css_data = _load_file(link["href"]).read()
link.clear()
if USING_PYTHON2:
link.string = css_data
else:
link.string = str(css_data)
link.name = "style"
del link["rel"]
del link["href"] | [
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12,002 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _bake_script | def _bake_script(script):
"""
Takes a script element and bakes it in only if it contains a remote resource
"""
if "src" in script.attrs:
if re.match("https?://", script["src"]):
script_data = _load_url(script["src"]).read()
else:
script_data = _load_file(script["src"]).read()
script.clear()
if USING_PYTHON2:
script.string = "\n" + script_data + "\n"
else:
script.string = "\n" + str(script_data) + "\n"
del script["src"]
del script["type"] | python | def _bake_script(script):
if "src" in script.attrs:
if re.match("https?://", script["src"]):
script_data = _load_url(script["src"]).read()
else:
script_data = _load_file(script["src"]).read()
script.clear()
if USING_PYTHON2:
script.string = "\n" + script_data + "\n"
else:
script.string = "\n" + str(script_data) + "\n"
del script["src"]
del script["type"] | [
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12,003 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _load_file | def _load_file(path):
"""
Loads a file from the local filesystem
"""
if not os.path.exists(path):
parser.error("{} was not found!".format(path))
if USING_PYTHON2:
mode = "r"
else:
mode = "rb"
try:
f = open(path, mode)
return f
except IOError as ex:
parser.error("{path} could not be read due to an I/O error! ({ex})".format(path=path, ex=ex)) | python | def _load_file(path):
if not os.path.exists(path):
parser.error("{} was not found!".format(path))
if USING_PYTHON2:
mode = "r"
else:
mode = "rb"
try:
f = open(path, mode)
return f
except IOError as ex:
parser.error("{path} could not be read due to an I/O error! ({ex})".format(path=path, ex=ex)) | [
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12,004 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _load_url | def _load_url(url):
"""
Loads a URL resource from a remote server
"""
try:
response = requests.get(url)
return BytesIO(response.content)
except IOError as ex:
parser.error("{url} could not be loaded remotely! ({ex})".format(url=url, ex=ex)) | python | def _load_url(url):
try:
response = requests.get(url)
return BytesIO(response.content)
except IOError as ex:
parser.error("{url} could not be loaded remotely! ({ex})".format(url=url, ex=ex)) | [
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12,005 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | _get_bs4_string | def _get_bs4_string(soup):
"""
Outputs a BeautifulSoup object as a string that should hopefully be minimally modified
"""
if len(soup.find_all("script")) == 0:
soup_str = soup.prettify(formatter=None).strip()
else:
soup_str = str(soup.html)
soup_str = re.sub("&", "&", soup_str)
soup_str = re.sub("<", "<", soup_str)
soup_str = re.sub(">", ">", soup_str)
return soup_str | python | def _get_bs4_string(soup):
if len(soup.find_all("script")) == 0:
soup_str = soup.prettify(formatter=None).strip()
else:
soup_str = str(soup.html)
soup_str = re.sub("&", "&", soup_str)
soup_str = re.sub("<", "<", soup_str)
soup_str = re.sub(">", ">", soup_str)
return soup_str | [
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12,006 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | bake | def bake(src):
"""
Runs the encoder on the given source file
"""
src = os.path.realpath(src)
path = os.path.dirname(src)
filename = os.path.basename(src)
html = _load_file(src).read()
if imghdr.what("", html):
html = "<html><body><img src='{}'/></body></html>".format(cgi.escape(filename))
# Change to the file's directory so image files with relative paths can be loaded correctly
cwd = os.getcwd()
os.chdir(path)
bs_html = bs4.BeautifulSoup(html, "html.parser")
images = bs_html.find_all("img")
for image in images:
_image_to_data(image)
for link in bs_html.find_all("link"):
_bake_css(link)
for script in bs_html.find_all("script"):
_bake_script(script)
os.chdir(cwd)
return bs_html | python | def bake(src):
src = os.path.realpath(src)
path = os.path.dirname(src)
filename = os.path.basename(src)
html = _load_file(src).read()
if imghdr.what("", html):
html = "<html><body><img src='{}'/></body></html>".format(cgi.escape(filename))
# Change to the file's directory so image files with relative paths can be loaded correctly
cwd = os.getcwd()
os.chdir(path)
bs_html = bs4.BeautifulSoup(html, "html.parser")
images = bs_html.find_all("img")
for image in images:
_image_to_data(image)
for link in bs_html.find_all("link"):
_bake_css(link)
for script in bs_html.find_all("script"):
_bake_script(script)
os.chdir(cwd)
return bs_html | [
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12,007 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | upload_html | def upload_html(destination, html, name=None):
"""
Uploads the HTML to a file on the server
"""
[project, path, n] = parse_destination(destination)
try:
dxfile = dxpy.upload_string(html, media_type="text/html", project=project, folder=path, hidden=True, name=name or None)
return dxfile.get_id()
except dxpy.DXAPIError as ex:
parser.error("Could not upload HTML report to DNAnexus server! ({ex})".format(ex=ex)) | python | def upload_html(destination, html, name=None):
[project, path, n] = parse_destination(destination)
try:
dxfile = dxpy.upload_string(html, media_type="text/html", project=project, folder=path, hidden=True, name=name or None)
return dxfile.get_id()
except dxpy.DXAPIError as ex:
parser.error("Could not upload HTML report to DNAnexus server! ({ex})".format(ex=ex)) | [
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12,008 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | create_record | def create_record(destination, file_ids, width=None, height=None):
"""
Creates a master record for the HTML report; this doesn't contain contain the actual HTML, but reports
are required to be records rather than files and we can link more than one HTML file to a report
"""
[project, path, name] = parse_destination(destination)
files = [dxpy.dxlink(file_id) for file_id in file_ids]
details = {"files": files}
if width:
details["width"] = width
if height:
details["height"] = height
try:
dxrecord = dxpy.new_dxrecord(project=project, folder=path, types=["Report", "HTMLReport"], details=details, name=name)
dxrecord.close()
return dxrecord.get_id()
except dxpy.DXAPIError as ex:
parser.error("Could not create an HTML report record on DNAnexus servers! ({ex})".format(ex=ex)) | python | def create_record(destination, file_ids, width=None, height=None):
[project, path, name] = parse_destination(destination)
files = [dxpy.dxlink(file_id) for file_id in file_ids]
details = {"files": files}
if width:
details["width"] = width
if height:
details["height"] = height
try:
dxrecord = dxpy.new_dxrecord(project=project, folder=path, types=["Report", "HTMLReport"], details=details, name=name)
dxrecord.close()
return dxrecord.get_id()
except dxpy.DXAPIError as ex:
parser.error("Could not create an HTML report record on DNAnexus servers! ({ex})".format(ex=ex)) | [
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12,009 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_report_html.py | save | def save(filename, html):
"""
Creates a baked HTML file on the local system
"""
try:
out_file = open(filename, "w")
out_file.write(html)
out_file.close()
except IOError as ex:
parser.error("Could not write baked HTML to local file {name}. ({ex})".format(name=filename, ex=ex)) | python | def save(filename, html):
try:
out_file = open(filename, "w")
out_file.write(html)
out_file.close()
except IOError as ex:
parser.error("Could not write baked HTML to local file {name}. ({ex})".format(name=filename, ex=ex)) | [
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12,010 | dnanexus/dx-toolkit | src/python/dxpy/utils/describe.py | get_size_str | def get_size_str(size):
"""
Formats a byte size as a string.
The returned string is no more than 9 characters long.
"""
if size == 0:
magnitude = 0
level = 0
else:
magnitude = math.floor(math.log(size, 10))
level = int(min(math.floor(magnitude // 3), 4))
return ('%d' if level == 0 else '%.2f') % (float(size) / 2**(level*10)) + ' ' + SIZE_LEVEL[level] | python | def get_size_str(size):
if size == 0:
magnitude = 0
level = 0
else:
magnitude = math.floor(math.log(size, 10))
level = int(min(math.floor(magnitude // 3), 4))
return ('%d' if level == 0 else '%.2f') % (float(size) / 2**(level*10)) + ' ' + SIZE_LEVEL[level] | [
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12,011 | dnanexus/dx-toolkit | src/python/dxpy/utils/describe.py | get_ls_l_desc | def get_ls_l_desc(desc, include_folder=False, include_project=False):
"""
desc must have at least all the fields given by get_ls_l_desc_fields.
"""
# If you make this method consume an additional field, you must add it to
# get_ls_l_desc_fields above.
if 'state' in desc:
state_len = len(desc['state'])
if desc['state'] != 'closed':
state_str = YELLOW() + desc['state'] + ENDC()
else:
state_str = GREEN() + desc['state'] + ENDC()
else:
state_str = ''
state_len = 0
name_str = ''
if include_folder:
name_str += desc['folder'] + ('/' if desc['folder'] != '/' else '')
name_str += desc['name']
if desc['class'] in ['applet', 'workflow']:
name_str = BOLD() + GREEN() + name_str + ENDC()
size_str = ''
if 'size' in desc and desc['class'] == 'file':
size_str = get_size_str(desc['size'])
elif 'length' in desc:
size_str = str(desc['length']) + ' rows'
size_padding = ' ' * max(0, 9 - len(size_str))
return (state_str +
DELIMITER(' '*(8 - state_len)) + render_short_timestamp(desc['modified']) +
DELIMITER(' ') + size_str +
DELIMITER(size_padding + ' ') + name_str +
DELIMITER(' (') + ((desc['project'] + DELIMITER(':')) if include_project else '') + desc['id'] +
DELIMITER(')')) | python | def get_ls_l_desc(desc, include_folder=False, include_project=False):
# If you make this method consume an additional field, you must add it to
# get_ls_l_desc_fields above.
if 'state' in desc:
state_len = len(desc['state'])
if desc['state'] != 'closed':
state_str = YELLOW() + desc['state'] + ENDC()
else:
state_str = GREEN() + desc['state'] + ENDC()
else:
state_str = ''
state_len = 0
name_str = ''
if include_folder:
name_str += desc['folder'] + ('/' if desc['folder'] != '/' else '')
name_str += desc['name']
if desc['class'] in ['applet', 'workflow']:
name_str = BOLD() + GREEN() + name_str + ENDC()
size_str = ''
if 'size' in desc and desc['class'] == 'file':
size_str = get_size_str(desc['size'])
elif 'length' in desc:
size_str = str(desc['length']) + ' rows'
size_padding = ' ' * max(0, 9 - len(size_str))
return (state_str +
DELIMITER(' '*(8 - state_len)) + render_short_timestamp(desc['modified']) +
DELIMITER(' ') + size_str +
DELIMITER(size_padding + ' ') + name_str +
DELIMITER(' (') + ((desc['project'] + DELIMITER(':')) if include_project else '') + desc['id'] +
DELIMITER(')')) | [
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12,012 | dnanexus/dx-toolkit | src/python/dxpy/utils/executable_unbuilder.py | _recursive_cleanup | def _recursive_cleanup(foo):
"""
Aggressively cleans up things that look empty.
"""
if isinstance(foo, dict):
for (key, val) in list(foo.items()):
if isinstance(val, dict):
_recursive_cleanup(val)
if val == "" or val == [] or val == {}:
del foo[key] | python | def _recursive_cleanup(foo):
if isinstance(foo, dict):
for (key, val) in list(foo.items()):
if isinstance(val, dict):
_recursive_cleanup(val)
if val == "" or val == [] or val == {}:
del foo[key] | [
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12,013 | dnanexus/dx-toolkit | src/python/dxpy/utils/executable_unbuilder.py | dump_executable | def dump_executable(executable, destination_directory, omit_resources=False, describe_output={}):
"""
Reconstitutes an app, applet, or a workflow into a directory that would
create a functionally identical executable if "dx build" were run on it.
destination_directory will be the root source directory for the
executable.
:param executable: executable, i.e. app, applet, or workflow, to be dumped
:type executable: DXExecutable (either of: DXApp, DXApplet, DXWorkflow, DXGlobalWorkflow)
:param destination_directory: an existing, empty, and writable directory
:type destination_directory: str
:param omit_resources: if True, executable's resources will not be downloaded
:type omit_resources: boolean
:param describe_output: output of a describe API call on the executable
:type describe_output: dictionary
"""
try:
old_cwd = os.getcwd()
os.chdir(destination_directory)
if isinstance(executable, dxpy.DXWorkflow):
_dump_workflow(executable, describe_output)
elif isinstance(executable, dxpy.DXGlobalWorkflow):
# Add inputs, outputs, stages. These fields contain region-specific values
# e.g. files or applets, that's why:
# * if the workflow is global, we will unpack the underlying workflow
# from the region of the current project context
# * if this is a regular, project-based workflow, we will just use
# its description (the describe_output that we already have)
# Underlying workflows are workflows stored in resource containers
# of the global workflow (one per each region the global workflow is
# enabled in). #TODO: add a link to documentation.
current_project = dxpy.WORKSPACE_ID
if not current_project:
raise DXError(
'A project needs to be selected to "dx get" a global workflow. You can use "dx select" to select a project')
region = dxpy.api.project_describe(current_project,
input_params={"fields": {"region": True}})["region"]
describe_output = executable.append_underlying_workflow_desc(describe_output, region)
_dump_workflow(executable, describe_output)
else:
_dump_app_or_applet(executable, omit_resources, describe_output)
except:
err_exit()
finally:
os.chdir(old_cwd) | python | def dump_executable(executable, destination_directory, omit_resources=False, describe_output={}):
try:
old_cwd = os.getcwd()
os.chdir(destination_directory)
if isinstance(executable, dxpy.DXWorkflow):
_dump_workflow(executable, describe_output)
elif isinstance(executable, dxpy.DXGlobalWorkflow):
# Add inputs, outputs, stages. These fields contain region-specific values
# e.g. files or applets, that's why:
# * if the workflow is global, we will unpack the underlying workflow
# from the region of the current project context
# * if this is a regular, project-based workflow, we will just use
# its description (the describe_output that we already have)
# Underlying workflows are workflows stored in resource containers
# of the global workflow (one per each region the global workflow is
# enabled in). #TODO: add a link to documentation.
current_project = dxpy.WORKSPACE_ID
if not current_project:
raise DXError(
'A project needs to be selected to "dx get" a global workflow. You can use "dx select" to select a project')
region = dxpy.api.project_describe(current_project,
input_params={"fields": {"region": True}})["region"]
describe_output = executable.append_underlying_workflow_desc(describe_output, region)
_dump_workflow(executable, describe_output)
else:
_dump_app_or_applet(executable, omit_resources, describe_output)
except:
err_exit()
finally:
os.chdir(old_cwd) | [
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12,014 | dnanexus/dx-toolkit | src/python/dxpy/bindings/download_all_inputs.py | _gen_helper_dict | def _gen_helper_dict(filtered_inputs):
'''
Create a dict of values for the downloaded files. This is similar to the variables created
when running a bash app.
'''
file_key_descs, _ignore = file_load_utils.analyze_bash_vars(
file_load_utils.get_input_json_file(), None)
flattened_dict = {}
def add_if_no_collision(key, value, dict_):
if key not in dict_:
dict_[key] = value
for input_ in filtered_inputs:
if input_ not in file_key_descs:
continue
input_var_dict = file_key_descs[input_]
add_if_no_collision(input_ + '_path', input_var_dict["path"], flattened_dict)
add_if_no_collision(input_ + '_name', input_var_dict["basename"], flattened_dict)
add_if_no_collision(input_ + '_prefix', input_var_dict["prefix"], flattened_dict)
return flattened_dict | python | def _gen_helper_dict(filtered_inputs):
'''
Create a dict of values for the downloaded files. This is similar to the variables created
when running a bash app.
'''
file_key_descs, _ignore = file_load_utils.analyze_bash_vars(
file_load_utils.get_input_json_file(), None)
flattened_dict = {}
def add_if_no_collision(key, value, dict_):
if key not in dict_:
dict_[key] = value
for input_ in filtered_inputs:
if input_ not in file_key_descs:
continue
input_var_dict = file_key_descs[input_]
add_if_no_collision(input_ + '_path', input_var_dict["path"], flattened_dict)
add_if_no_collision(input_ + '_name', input_var_dict["basename"], flattened_dict)
add_if_no_collision(input_ + '_prefix', input_var_dict["prefix"], flattened_dict)
return flattened_dict | [
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12,015 | dnanexus/dx-toolkit | src/python/dxpy/bindings/download_all_inputs.py | _get_num_parallel_threads | def _get_num_parallel_threads(max_threads, num_cores, mem_available_mb):
'''
Ensure at least ~1.2 GB memory per thread, see PTFM-18767
'''
return min(max_threads, num_cores, max(int(mem_available_mb/1200), 1)) | python | def _get_num_parallel_threads(max_threads, num_cores, mem_available_mb):
'''
Ensure at least ~1.2 GB memory per thread, see PTFM-18767
'''
return min(max_threads, num_cores, max(int(mem_available_mb/1200), 1)) | [
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12,016 | dnanexus/dx-toolkit | doc/examples/dx-apps/report_example/src/report_example.py | main | def main(**kwargs):
"""
Draw a couple of simple graphs and optionally generate an HTML file to upload them
"""
draw_lines()
draw_histogram()
draw_bar_chart()
destination = "-r /report"
if use_html:
generate_html()
command = "dx-build-report-html {h} {d}".format(h=html_filename, d=destination)
else:
command = "dx-build-report-html {l} {b} {h} {d}".format(l=lines_filename, b=bars_filename, h=histogram_filename, d=destination)
sub_output = json.loads(subprocess.check_output(command, shell=True))
output = {}
output["report"] = dxpy.dxlink(sub_output["recordId"])
return output | python | def main(**kwargs):
draw_lines()
draw_histogram()
draw_bar_chart()
destination = "-r /report"
if use_html:
generate_html()
command = "dx-build-report-html {h} {d}".format(h=html_filename, d=destination)
else:
command = "dx-build-report-html {l} {b} {h} {d}".format(l=lines_filename, b=bars_filename, h=histogram_filename, d=destination)
sub_output = json.loads(subprocess.check_output(command, shell=True))
output = {}
output["report"] = dxpy.dxlink(sub_output["recordId"])
return output | [
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12,017 | dnanexus/dx-toolkit | doc/examples/dx-apps/report_example/src/report_example.py | draw_lines | def draw_lines():
"""
Draws a line between a set of random values
"""
r = numpy.random.randn(200)
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.plot(r)
ax.grid(True)
pyplot.savefig(lines_filename) | python | def draw_lines():
r = numpy.random.randn(200)
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.plot(r)
ax.grid(True)
pyplot.savefig(lines_filename) | [
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12,018 | dnanexus/dx-toolkit | doc/examples/dx-apps/report_example/src/report_example.py | generate_html | def generate_html():
"""
Generate an HTML file incorporating the images produced by this script
"""
html_file = open(html_filename, "w")
html_file.write("<html><body>")
html_file.write("<h1>Here are some graphs for you!</h1>")
for image in [lines_filename, bars_filename, histogram_filename]:
html_file.write("<div><h2>{0}</h2><img src='{0}' /></div>".format(image))
html_file.write("</body></html>")
html_file.close() | python | def generate_html():
html_file = open(html_filename, "w")
html_file.write("<html><body>")
html_file.write("<h1>Here are some graphs for you!</h1>")
for image in [lines_filename, bars_filename, histogram_filename]:
html_file.write("<div><h2>{0}</h2><img src='{0}' /></div>".format(image))
html_file.write("</body></html>")
html_file.close() | [
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12,019 | dnanexus/dx-toolkit | src/python/dxpy/utils/exec_utils.py | _safe_unicode | def _safe_unicode(o):
"""
Returns an equivalent unicode object, trying harder to avoid
dependencies on the Python default encoding.
"""
def clean(s):
return u''.join([c if c in ASCII_PRINTABLE else '?' for c in s])
if USING_PYTHON2:
try:
return unicode(o)
except:
try:
s = str(o)
try:
return s.decode("utf-8")
except:
return clean(s[:2048]) + u" [Raw error message: " + unicode(s.encode("hex"), 'utf-8') + u"]"
except:
return u"(Unable to decode Python exception message)"
else:
return str(o) | python | def _safe_unicode(o):
def clean(s):
return u''.join([c if c in ASCII_PRINTABLE else '?' for c in s])
if USING_PYTHON2:
try:
return unicode(o)
except:
try:
s = str(o)
try:
return s.decode("utf-8")
except:
return clean(s[:2048]) + u" [Raw error message: " + unicode(s.encode("hex"), 'utf-8') + u"]"
except:
return u"(Unable to decode Python exception message)"
else:
return str(o) | [
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12,020 | dnanexus/dx-toolkit | src/python/dxpy/utils/exec_utils.py | _format_exception_message | def _format_exception_message(e):
"""
Formats the specified exception.
"""
# Prevent duplication of "AppError" in places that print "AppError"
# and then this formatted string
if isinstance(e, dxpy.AppError):
return _safe_unicode(e)
if USING_PYTHON2:
return unicode(e.__class__.__name__, 'utf-8') + ": " + _safe_unicode(e)
else:
return e.__class__.__name__ + ": " + _safe_unicode(e) | python | def _format_exception_message(e):
# Prevent duplication of "AppError" in places that print "AppError"
# and then this formatted string
if isinstance(e, dxpy.AppError):
return _safe_unicode(e)
if USING_PYTHON2:
return unicode(e.__class__.__name__, 'utf-8') + ": " + _safe_unicode(e)
else:
return e.__class__.__name__ + ": " + _safe_unicode(e) | [
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12,021 | dnanexus/dx-toolkit | src/python/dxpy/utils/exec_utils.py | run | def run(function_name=None, function_input=None):
"""Triggers the execution environment entry point processor.
Use this function in the program entry point code:
.. code-block:: python
import dxpy
@dxpy.entry_point('main')
def hello(i):
pass
dxpy.run()
This method may be used to invoke the program either in a production
environment (inside the execution environment) or for local
debugging (in the debug harness), as follows:
If the environment variable *DX_JOB_ID* is set, the processor
retrieves the job with that ID from the API server. The job's
*function* field indicates the function name to be invoked. That
function name is looked up in the table of all methods decorated
with *@dxpy.entry_point('name')* in the module from which
:func:`run()` was called, and the matching method is invoked (with
the job's input supplied as parameters). This is the mode of
operation used in the DNAnexus execution environment.
.. warning::
The parameters *function_name* and *function_input* are
disregarded in this mode of operation.
If the environment variable *DX_JOB_ID* is not set, the function
name may be given in *function_name*; if not set, it is set by the
environment variable *DX_TEST_FUNCTION*. The function input may be
given in *function_input*; if not set, it is set by the local file
*job_input.json* which is expected to be present.
The absence of *DX_JOB_ID* signals to :func:`run()` that execution
is happening in the debug harness. In this mode of operation, all
calls to :func:`dxpy.bindings.dxjob.new_dxjob()` (and higher level
handler methods which use it) are intercepted, and :func:`run()` is
invoked instead with appropriate inputs.
"""
global RUN_COUNT
RUN_COUNT += 1
dx_working_dir = os.getcwd()
if dxpy.JOB_ID is not None:
logging.basicConfig()
try:
logging.getLogger().addHandler(dxpy.DXLogHandler())
except dxpy.exceptions.DXError:
print("TODO: FIXME: the EE client should die if logging is not available")
job = dxpy.describe(dxpy.JOB_ID)
else:
if function_name is None:
function_name = os.environ.get('DX_TEST_FUNCTION', 'main')
if function_input is None:
with open("job_input.json", "r") as fh:
function_input = json.load(fh)
job = {'function': function_name, 'input': function_input}
with open("job_error_reserved_space", "w") as fh:
fh.write("This file contains reserved space for writing job errors in case the filesystem becomes full.\n" + " "*1024*64)
print("Invoking", job.get('function'), "with", job.get('input'))
try:
result = ENTRY_POINT_TABLE[job['function']](**job['input'])
except dxpy.AppError as e:
save_error(e, dx_working_dir, error_type="AppError")
raise
except Exception as e:
save_error(e, dx_working_dir)
raise
if result is not None:
# TODO: protect against client removing its original working directory
os.chdir(dx_working_dir)
if USING_PYTHON2:
# On python-2 we need to use binary mode
with open("job_output.json", "wb") as fh:
json.dump(result, fh, indent=2, cls=DXJSONEncoder)
fh.write(b"\n")
else:
with open("job_output.json", "w") as fh:
json.dump(result, fh, indent=2, cls=DXJSONEncoder)
fh.write("\n")
return result | python | def run(function_name=None, function_input=None):
global RUN_COUNT
RUN_COUNT += 1
dx_working_dir = os.getcwd()
if dxpy.JOB_ID is not None:
logging.basicConfig()
try:
logging.getLogger().addHandler(dxpy.DXLogHandler())
except dxpy.exceptions.DXError:
print("TODO: FIXME: the EE client should die if logging is not available")
job = dxpy.describe(dxpy.JOB_ID)
else:
if function_name is None:
function_name = os.environ.get('DX_TEST_FUNCTION', 'main')
if function_input is None:
with open("job_input.json", "r") as fh:
function_input = json.load(fh)
job = {'function': function_name, 'input': function_input}
with open("job_error_reserved_space", "w") as fh:
fh.write("This file contains reserved space for writing job errors in case the filesystem becomes full.\n" + " "*1024*64)
print("Invoking", job.get('function'), "with", job.get('input'))
try:
result = ENTRY_POINT_TABLE[job['function']](**job['input'])
except dxpy.AppError as e:
save_error(e, dx_working_dir, error_type="AppError")
raise
except Exception as e:
save_error(e, dx_working_dir)
raise
if result is not None:
# TODO: protect against client removing its original working directory
os.chdir(dx_working_dir)
if USING_PYTHON2:
# On python-2 we need to use binary mode
with open("job_output.json", "wb") as fh:
json.dump(result, fh, indent=2, cls=DXJSONEncoder)
fh.write(b"\n")
else:
with open("job_output.json", "w") as fh:
json.dump(result, fh, indent=2, cls=DXJSONEncoder)
fh.write("\n")
return result | [
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Use this function in the program entry point code:
.. code-block:: python
import dxpy
@dxpy.entry_point('main')
def hello(i):
pass
dxpy.run()
This method may be used to invoke the program either in a production
environment (inside the execution environment) or for local
debugging (in the debug harness), as follows:
If the environment variable *DX_JOB_ID* is set, the processor
retrieves the job with that ID from the API server. The job's
*function* field indicates the function name to be invoked. That
function name is looked up in the table of all methods decorated
with *@dxpy.entry_point('name')* in the module from which
:func:`run()` was called, and the matching method is invoked (with
the job's input supplied as parameters). This is the mode of
operation used in the DNAnexus execution environment.
.. warning::
The parameters *function_name* and *function_input* are
disregarded in this mode of operation.
If the environment variable *DX_JOB_ID* is not set, the function
name may be given in *function_name*; if not set, it is set by the
environment variable *DX_TEST_FUNCTION*. The function input may be
given in *function_input*; if not set, it is set by the local file
*job_input.json* which is expected to be present.
The absence of *DX_JOB_ID* signals to :func:`run()` that execution
is happening in the debug harness. In this mode of operation, all
calls to :func:`dxpy.bindings.dxjob.new_dxjob()` (and higher level
handler methods which use it) are intercepted, and :func:`run()` is
invoked instead with appropriate inputs. | [
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] | 74befb53ad90fcf902d8983ae6d74580f402d619 | https://github.com/dnanexus/dx-toolkit/blob/74befb53ad90fcf902d8983ae6d74580f402d619/src/python/dxpy/utils/exec_utils.py#L74-L171 |
12,022 | dnanexus/dx-toolkit | src/python/dxpy/utils/exec_utils.py | entry_point | def entry_point(entry_point_name):
"""Use this to decorate a DNAnexus execution environment entry point.
Example:
.. code-block:: python
@dxpy.entry_point('main')
def hello(i):
pass
"""
def wrap(f):
ENTRY_POINT_TABLE[entry_point_name] = f
@wraps(f)
def wrapped_f(*args, **kwargs):
return f(*args, **kwargs)
return wrapped_f
return wrap | python | def entry_point(entry_point_name):
def wrap(f):
ENTRY_POINT_TABLE[entry_point_name] = f
@wraps(f)
def wrapped_f(*args, **kwargs):
return f(*args, **kwargs)
return wrapped_f
return wrap | [
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... | Use this to decorate a DNAnexus execution environment entry point.
Example:
.. code-block:: python
@dxpy.entry_point('main')
def hello(i):
pass | [
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12,023 | dnanexus/dx-toolkit | src/python/dxpy/bindings/auth.py | user_info | def user_info(authserver_host=None, authserver_port=None):
"""Returns the result of the user_info call against the specified auth
server.
.. deprecated:: 0.108.0
Use :func:`whoami` instead where possible.
"""
authserver = get_auth_server_name(authserver_host, authserver_port)
return DXHTTPRequest(authserver + "/system/getUserInfo", {}, prepend_srv=False) | python | def user_info(authserver_host=None, authserver_port=None):
authserver = get_auth_server_name(authserver_host, authserver_port)
return DXHTTPRequest(authserver + "/system/getUserInfo", {}, prepend_srv=False) | [
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.. deprecated:: 0.108.0
Use :func:`whoami` instead where possible. | [
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12,024 | dnanexus/dx-toolkit | src/python/dxpy/utils/file_load_utils.py | get_job_input_filenames | def get_job_input_filenames(job_input_file):
"""Extract list of files, returns a set of directories to create, and
a set of files, with sources and destinations. The paths created are
relative to the input directory.
Note: we go through file names inside arrays, and create a
separate subdirectory for each. This avoids clobbering files when
duplicate filenames appear in an array.
"""
def get_input_hash():
with open(job_input_file) as fh:
job_input = json.load(fh)
return job_input
job_input = get_input_hash()
files = collections.defaultdict(list) # dictionary, with empty lists as default elements
dirs = [] # directories to create under <idir>
# Local function for adding a file to the list of files to be created
# for example:
# iname == "seq1"
# subdir == "015"
# value == { "$dnanexus_link": {
# "project": "project-BKJfY1j0b06Z4y8PX8bQ094f",
# "id": "file-BKQGkgQ0b06xG5560GGQ001B"
# }
# will create a record describing that the file should
# be downloaded into seq1/015/<filename>
def add_file(iname, subdir, value):
if not dxpy.is_dxlink(value):
return
handler = dxpy.get_handler(value)
if not isinstance(handler, dxpy.DXFile):
return
filename = make_unix_filename(handler.name)
trg_dir = iname
if subdir is not None:
trg_dir = os.path.join(trg_dir, subdir)
files[iname].append({'trg_fname': os.path.join(trg_dir, filename),
'handler': handler,
'src_file_id': handler.id})
dirs.append(trg_dir)
# An array of inputs, for a single key. A directory
# will be created per array entry. For example, if the input key is
# FOO, and the inputs are {A, B, C}.vcf then, the directory structure
# will be:
# <idir>/FOO/00/A.vcf
# <idir>/FOO/01/B.vcf
# <idir>/FOO/02/C.vcf
def add_file_array(input_name, links):
num_files = len(links)
if num_files == 0:
return
num_digits = len(str(num_files - 1))
dirs.append(input_name)
for i, link in enumerate(links):
subdir = str(i).zfill(num_digits)
add_file(input_name, subdir, link)
for input_name, value in list(job_input.items()):
if isinstance(value, list):
# This is a file array
add_file_array(input_name, value)
else:
add_file(input_name, None, value)
## create a dictionary of the all non-file elements
rest_hash = {key: val for key, val in list(job_input.items()) if key not in files}
return dirs, files, rest_hash | python | def get_job_input_filenames(job_input_file):
def get_input_hash():
with open(job_input_file) as fh:
job_input = json.load(fh)
return job_input
job_input = get_input_hash()
files = collections.defaultdict(list) # dictionary, with empty lists as default elements
dirs = [] # directories to create under <idir>
# Local function for adding a file to the list of files to be created
# for example:
# iname == "seq1"
# subdir == "015"
# value == { "$dnanexus_link": {
# "project": "project-BKJfY1j0b06Z4y8PX8bQ094f",
# "id": "file-BKQGkgQ0b06xG5560GGQ001B"
# }
# will create a record describing that the file should
# be downloaded into seq1/015/<filename>
def add_file(iname, subdir, value):
if not dxpy.is_dxlink(value):
return
handler = dxpy.get_handler(value)
if not isinstance(handler, dxpy.DXFile):
return
filename = make_unix_filename(handler.name)
trg_dir = iname
if subdir is not None:
trg_dir = os.path.join(trg_dir, subdir)
files[iname].append({'trg_fname': os.path.join(trg_dir, filename),
'handler': handler,
'src_file_id': handler.id})
dirs.append(trg_dir)
# An array of inputs, for a single key. A directory
# will be created per array entry. For example, if the input key is
# FOO, and the inputs are {A, B, C}.vcf then, the directory structure
# will be:
# <idir>/FOO/00/A.vcf
# <idir>/FOO/01/B.vcf
# <idir>/FOO/02/C.vcf
def add_file_array(input_name, links):
num_files = len(links)
if num_files == 0:
return
num_digits = len(str(num_files - 1))
dirs.append(input_name)
for i, link in enumerate(links):
subdir = str(i).zfill(num_digits)
add_file(input_name, subdir, link)
for input_name, value in list(job_input.items()):
if isinstance(value, list):
# This is a file array
add_file_array(input_name, value)
else:
add_file(input_name, None, value)
## create a dictionary of the all non-file elements
rest_hash = {key: val for key, val in list(job_input.items()) if key not in files}
return dirs, files, rest_hash | [
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a set of files, with sources and destinations. The paths created are
relative to the input directory.
Note: we go through file names inside arrays, and create a
separate subdirectory for each. This avoids clobbering files when
duplicate filenames appear in an array. | [
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12,025 | dnanexus/dx-toolkit | src/python/dxpy/utils/file_load_utils.py | analyze_bash_vars | def analyze_bash_vars(job_input_file, job_homedir):
'''
This function examines the input file, and calculates variables to
instantiate in the shell environment. It is called right before starting the
execution of an app in a worker.
For each input key, we want to have
$var
$var_filename
$var_prefix
remove last dot (+gz), and/or remove patterns
$var_path
$HOME/in/var/$var_filename
For example,
$HOME/in/genes/A.txt
B.txt
export genes=('{"$dnanexus_link": "file-xxxx"}' '{"$dnanexus_link": "file-yyyy"}')
export genes_filename=("A.txt" "B.txt")
export genes_prefix=("A" "B")
export genes_path=("$HOME/in/genes/A.txt" "$HOME/in/genes/B.txt")
If there are patterns defined in the input spec, then the prefix respects them.
Here are several examples, where the patterns are:
*.bam, *.bwa-index.tar.gz, foo*.sam, z*ra.sam
file name prefix matches
foo.zed.bam foo.zed *.bam
xxx.bwa-index.tar.gz xxx *.bwa-index.tar.gz
food.sam food foo*.sam
zebra.sam zebra z*ra.sam
xx.c xx
xx.c.gz xx
The only patterns we recognize are of the form x*.y. For example:
legal *.sam, *.c.py, foo*.sam, a*b*c.baz
ignored uu.txt x???.tar mon[a-z].py
'''
_, file_entries, rest_hash = get_job_input_filenames(job_input_file)
patterns_dict = get_input_spec_patterns()
# Note: there may be multiple matches, choose the shortest prefix.
def get_prefix(basename, key):
best_prefix = None
patterns = patterns_dict.get(key)
if patterns is not None:
for pattern in patterns:
if fnmatch.fnmatch(basename, pattern):
_, _, right_piece = pattern.rpartition("*")
best_prefix = choose_shorter_string(best_prefix, basename[:-len(right_piece)])
if best_prefix is not None:
return best_prefix
else:
# no matching rule
parts = os.path.splitext(basename)
if parts[1] == ".gz":
parts = os.path.splitext(parts[0])
return parts[0]
def factory():
return {'handler': [], 'basename': [], 'prefix': [], 'path': []}
file_key_descs = collections.defaultdict(factory)
rel_home_dir = get_input_dir(job_homedir)
for key, entries in list(file_entries.items()):
for entry in entries:
filename = entry['trg_fname']
basename = os.path.basename(filename)
prefix = get_prefix(basename, key)
k_desc = file_key_descs[key]
k_desc['handler'].append(entry['handler'])
k_desc['basename'].append(basename)
k_desc['prefix'].append(prefix)
k_desc['path'].append(os.path.join(rel_home_dir, filename))
return file_key_descs, rest_hash | python | def analyze_bash_vars(job_input_file, job_homedir):
'''
This function examines the input file, and calculates variables to
instantiate in the shell environment. It is called right before starting the
execution of an app in a worker.
For each input key, we want to have
$var
$var_filename
$var_prefix
remove last dot (+gz), and/or remove patterns
$var_path
$HOME/in/var/$var_filename
For example,
$HOME/in/genes/A.txt
B.txt
export genes=('{"$dnanexus_link": "file-xxxx"}' '{"$dnanexus_link": "file-yyyy"}')
export genes_filename=("A.txt" "B.txt")
export genes_prefix=("A" "B")
export genes_path=("$HOME/in/genes/A.txt" "$HOME/in/genes/B.txt")
If there are patterns defined in the input spec, then the prefix respects them.
Here are several examples, where the patterns are:
*.bam, *.bwa-index.tar.gz, foo*.sam, z*ra.sam
file name prefix matches
foo.zed.bam foo.zed *.bam
xxx.bwa-index.tar.gz xxx *.bwa-index.tar.gz
food.sam food foo*.sam
zebra.sam zebra z*ra.sam
xx.c xx
xx.c.gz xx
The only patterns we recognize are of the form x*.y. For example:
legal *.sam, *.c.py, foo*.sam, a*b*c.baz
ignored uu.txt x???.tar mon[a-z].py
'''
_, file_entries, rest_hash = get_job_input_filenames(job_input_file)
patterns_dict = get_input_spec_patterns()
# Note: there may be multiple matches, choose the shortest prefix.
def get_prefix(basename, key):
best_prefix = None
patterns = patterns_dict.get(key)
if patterns is not None:
for pattern in patterns:
if fnmatch.fnmatch(basename, pattern):
_, _, right_piece = pattern.rpartition("*")
best_prefix = choose_shorter_string(best_prefix, basename[:-len(right_piece)])
if best_prefix is not None:
return best_prefix
else:
# no matching rule
parts = os.path.splitext(basename)
if parts[1] == ".gz":
parts = os.path.splitext(parts[0])
return parts[0]
def factory():
return {'handler': [], 'basename': [], 'prefix': [], 'path': []}
file_key_descs = collections.defaultdict(factory)
rel_home_dir = get_input_dir(job_homedir)
for key, entries in list(file_entries.items()):
for entry in entries:
filename = entry['trg_fname']
basename = os.path.basename(filename)
prefix = get_prefix(basename, key)
k_desc = file_key_descs[key]
k_desc['handler'].append(entry['handler'])
k_desc['basename'].append(basename)
k_desc['prefix'].append(prefix)
k_desc['path'].append(os.path.join(rel_home_dir, filename))
return file_key_descs, rest_hash | [
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For each input key, we want to have
$var
$var_filename
$var_prefix
remove last dot (+gz), and/or remove patterns
$var_path
$HOME/in/var/$var_filename
For example,
$HOME/in/genes/A.txt
B.txt
export genes=('{"$dnanexus_link": "file-xxxx"}' '{"$dnanexus_link": "file-yyyy"}')
export genes_filename=("A.txt" "B.txt")
export genes_prefix=("A" "B")
export genes_path=("$HOME/in/genes/A.txt" "$HOME/in/genes/B.txt")
If there are patterns defined in the input spec, then the prefix respects them.
Here are several examples, where the patterns are:
*.bam, *.bwa-index.tar.gz, foo*.sam, z*ra.sam
file name prefix matches
foo.zed.bam foo.zed *.bam
xxx.bwa-index.tar.gz xxx *.bwa-index.tar.gz
food.sam food foo*.sam
zebra.sam zebra z*ra.sam
xx.c xx
xx.c.gz xx
The only patterns we recognize are of the form x*.y. For example:
legal *.sam, *.c.py, foo*.sam, a*b*c.baz
ignored uu.txt x???.tar mon[a-z].py | [
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12,026 | dnanexus/dx-toolkit | src/python/dxpy/utils/__init__.py | wait_for_a_future | def wait_for_a_future(futures, print_traceback=False):
"""
Return the next future that completes. If a KeyboardInterrupt is
received, then the entire process is exited immediately. See
wait_for_all_futures for more notes.
"""
while True:
try:
future = next(concurrent.futures.as_completed(futures, timeout=THREAD_TIMEOUT_MAX))
break
except concurrent.futures.TimeoutError:
pass
except KeyboardInterrupt:
if print_traceback:
traceback.print_stack()
else:
print('')
os._exit(os.EX_IOERR)
return future | python | def wait_for_a_future(futures, print_traceback=False):
while True:
try:
future = next(concurrent.futures.as_completed(futures, timeout=THREAD_TIMEOUT_MAX))
break
except concurrent.futures.TimeoutError:
pass
except KeyboardInterrupt:
if print_traceback:
traceback.print_stack()
else:
print('')
os._exit(os.EX_IOERR)
return future | [
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12,027 | dnanexus/dx-toolkit | src/python/dxpy/utils/__init__.py | _dict_raise_on_duplicates | def _dict_raise_on_duplicates(ordered_pairs):
"""
Reject duplicate keys.
"""
d = {}
for k, v in ordered_pairs:
if k in d:
raise ValueError("duplicate key: %r" % (k,))
else:
d[k] = v
return d | python | def _dict_raise_on_duplicates(ordered_pairs):
d = {}
for k, v in ordered_pairs:
if k in d:
raise ValueError("duplicate key: %r" % (k,))
else:
d[k] = v
return d | [
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12,028 | dnanexus/dx-toolkit | src/python/dxpy/executable_builder.py | assert_consistent_reg_options | def assert_consistent_reg_options(exec_type, json_spec, executable_builder_exeception):
"""
Validates the "regionalOptions" field and verifies all the regions used
in "regionalOptions" have the same options.
"""
reg_options_spec = json_spec.get('regionalOptions')
json_fn = 'dxapp.json' if exec_type == 'app' else 'dxworkflow.json'
if not isinstance(reg_options_spec, dict):
raise executable_builder_exeception("The field 'regionalOptions' in must be a mapping")
if not reg_options_spec:
raise executable_builder_exeception(
"The field 'regionalOptions' in " + json_fn + " must be a non-empty mapping")
regional_options_list = list(reg_options_spec.items())
for region, opts_for_region in regional_options_list:
if not isinstance(opts_for_region, dict):
raise executable_builder_exeception("The field 'regionalOptions['" + region +
"']' in " + json_fn + " must be a mapping")
if set(opts_for_region.keys()) != set(regional_options_list[0][1].keys()):
if set(opts_for_region.keys()) - set(regional_options_list[0][1].keys()):
with_key, without_key = region, regional_options_list[0][0]
key_name = next(iter(set(opts_for_region.keys()) - set(regional_options_list[0][1].keys())))
else:
with_key, without_key = regional_options_list[0][0], region
key_name = next(iter(set(regional_options_list[0][1].keys()) - set(opts_for_region.keys())))
raise executable_builder_exeception(
"All regions in regionalOptions must specify the same options; " +
"%s was given for %s but not for %s" % (key_name, with_key, without_key)
)
if exec_type == 'app':
for key in opts_for_region:
if key in json_spec.get('runSpec', {}):
raise executable_builder_exeception(
key + " cannot be given in both runSpec and in regional options for " + region) | python | def assert_consistent_reg_options(exec_type, json_spec, executable_builder_exeception):
reg_options_spec = json_spec.get('regionalOptions')
json_fn = 'dxapp.json' if exec_type == 'app' else 'dxworkflow.json'
if not isinstance(reg_options_spec, dict):
raise executable_builder_exeception("The field 'regionalOptions' in must be a mapping")
if not reg_options_spec:
raise executable_builder_exeception(
"The field 'regionalOptions' in " + json_fn + " must be a non-empty mapping")
regional_options_list = list(reg_options_spec.items())
for region, opts_for_region in regional_options_list:
if not isinstance(opts_for_region, dict):
raise executable_builder_exeception("The field 'regionalOptions['" + region +
"']' in " + json_fn + " must be a mapping")
if set(opts_for_region.keys()) != set(regional_options_list[0][1].keys()):
if set(opts_for_region.keys()) - set(regional_options_list[0][1].keys()):
with_key, without_key = region, regional_options_list[0][0]
key_name = next(iter(set(opts_for_region.keys()) - set(regional_options_list[0][1].keys())))
else:
with_key, without_key = regional_options_list[0][0], region
key_name = next(iter(set(regional_options_list[0][1].keys()) - set(opts_for_region.keys())))
raise executable_builder_exeception(
"All regions in regionalOptions must specify the same options; " +
"%s was given for %s but not for %s" % (key_name, with_key, without_key)
)
if exec_type == 'app':
for key in opts_for_region:
if key in json_spec.get('runSpec', {}):
raise executable_builder_exeception(
key + " cannot be given in both runSpec and in regional options for " + region) | [
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12,029 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_app.py | _check_suggestions | def _check_suggestions(app_json, publish=False):
"""
Examines the specified dxapp.json file and warns about any
violations of suggestions guidelines.
:raises: AppBuilderException for data objects that could not be found
"""
for input_field in app_json.get('inputSpec', []):
for suggestion in input_field.get('suggestions', []):
if 'project' in suggestion:
try:
project = dxpy.api.project_describe(suggestion['project'], {"permissions": True})
if 'PUBLIC' not in project['permissions'] and publish:
logger.warn('Project {name} NOT PUBLIC!'.format(name=project['name']))
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
logger.warn('Suggested project {name} does not exist, or not accessible by user'.format(
name=suggestion['project']))
if 'path' in suggestion:
try:
check_folder_exists(suggestion['project'], suggestion['path'], '')
except ResolutionError as e:
logger.warn('Folder {path} could not be found in project {project}'.format(
path=suggestion['path'], project=suggestion['project']))
if '$dnanexus_link' in suggestion:
if suggestion['$dnanexus_link'].startswith(('file-', 'record-')):
try:
dnanexus_link = dxpy.describe(suggestion['$dnanexus_link'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
raise dxpy.app_builder.AppBuilderException(
'Suggested object {name} could not be found'.format(
name=suggestion['$dnanexus_link']))
except Exception as e:
raise dxpy.app_builder.AppBuilderException(str(e))
if 'value' in suggestion and isinstance(suggestion["value"], dict):
if '$dnanexus_link' in suggestion['value']:
# Check if we have JSON or string
if isinstance(suggestion['value']['$dnanexus_link'], dict):
if 'project' in suggestion['value']['$dnanexus_link']:
try:
dxpy.api.project_describe(suggestion['value']['$dnanexus_link']['project'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
logger.warn('Suggested project {name} does not exist, or not accessible by user'.format(
name=suggestion['value']['$dnanexus_link']['project']))
elif isinstance(suggestion['value']['$dnanexus_link'], basestring):
if suggestion['value']['$dnanexus_link'].startswith(('file-', 'record-')):
try:
dnanexus_link = dxpy.describe(suggestion['value']['$dnanexus_link'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
raise dxpy.app_builder.AppBuilderException(
'Suggested object {name} could not be found'.format(
name=suggestion['value']['$dnanexus_link']))
except Exception as e:
raise dxpy.app_builder.AppBuilderException(str(e)) | python | def _check_suggestions(app_json, publish=False):
for input_field in app_json.get('inputSpec', []):
for suggestion in input_field.get('suggestions', []):
if 'project' in suggestion:
try:
project = dxpy.api.project_describe(suggestion['project'], {"permissions": True})
if 'PUBLIC' not in project['permissions'] and publish:
logger.warn('Project {name} NOT PUBLIC!'.format(name=project['name']))
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
logger.warn('Suggested project {name} does not exist, or not accessible by user'.format(
name=suggestion['project']))
if 'path' in suggestion:
try:
check_folder_exists(suggestion['project'], suggestion['path'], '')
except ResolutionError as e:
logger.warn('Folder {path} could not be found in project {project}'.format(
path=suggestion['path'], project=suggestion['project']))
if '$dnanexus_link' in suggestion:
if suggestion['$dnanexus_link'].startswith(('file-', 'record-')):
try:
dnanexus_link = dxpy.describe(suggestion['$dnanexus_link'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
raise dxpy.app_builder.AppBuilderException(
'Suggested object {name} could not be found'.format(
name=suggestion['$dnanexus_link']))
except Exception as e:
raise dxpy.app_builder.AppBuilderException(str(e))
if 'value' in suggestion and isinstance(suggestion["value"], dict):
if '$dnanexus_link' in suggestion['value']:
# Check if we have JSON or string
if isinstance(suggestion['value']['$dnanexus_link'], dict):
if 'project' in suggestion['value']['$dnanexus_link']:
try:
dxpy.api.project_describe(suggestion['value']['$dnanexus_link']['project'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
logger.warn('Suggested project {name} does not exist, or not accessible by user'.format(
name=suggestion['value']['$dnanexus_link']['project']))
elif isinstance(suggestion['value']['$dnanexus_link'], basestring):
if suggestion['value']['$dnanexus_link'].startswith(('file-', 'record-')):
try:
dnanexus_link = dxpy.describe(suggestion['value']['$dnanexus_link'])
except dxpy.exceptions.DXAPIError as e:
if e.code == 404:
raise dxpy.app_builder.AppBuilderException(
'Suggested object {name} could not be found'.format(
name=suggestion['value']['$dnanexus_link']))
except Exception as e:
raise dxpy.app_builder.AppBuilderException(str(e)) | [
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12,030 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_app.py | _check_syntax | def _check_syntax(code, lang, temp_dir, enforce=True):
"""
Checks that the code whose text is in CODE parses as LANG.
Raises DXSyntaxError if there is a problem and "enforce" is True.
"""
# This function needs the language to be explicitly set, so we can
# generate an appropriate temp filename.
if lang == 'python2.7':
temp_basename = 'inlined_code_from_dxapp_json.py'
elif lang == 'bash':
temp_basename = 'inlined_code_from_dxapp_json.sh'
else:
raise ValueError('lang must be one of "python2.7" or "bash"')
# Dump the contents out to a temporary file, then call _check_file_syntax.
with open(os.path.join(temp_dir, temp_basename), 'w') as ofile:
ofile.write(code)
_check_file_syntax(os.path.join(temp_dir, temp_basename), temp_dir, override_lang=lang, enforce=enforce) | python | def _check_syntax(code, lang, temp_dir, enforce=True):
# This function needs the language to be explicitly set, so we can
# generate an appropriate temp filename.
if lang == 'python2.7':
temp_basename = 'inlined_code_from_dxapp_json.py'
elif lang == 'bash':
temp_basename = 'inlined_code_from_dxapp_json.sh'
else:
raise ValueError('lang must be one of "python2.7" or "bash"')
# Dump the contents out to a temporary file, then call _check_file_syntax.
with open(os.path.join(temp_dir, temp_basename), 'w') as ofile:
ofile.write(code)
_check_file_syntax(os.path.join(temp_dir, temp_basename), temp_dir, override_lang=lang, enforce=enforce) | [
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12,031 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_app.py | _check_file_syntax | def _check_file_syntax(filename, temp_dir, override_lang=None, enforce=True):
"""
Checks that the code in FILENAME parses, attempting to autodetect
the language if necessary.
Raises IOError if the file cannot be read.
Raises DXSyntaxError if there is a problem and "enforce" is True.
"""
def check_python(filename):
# Generate a semi-recognizable name to write the pyc to. Of
# course it's possible that different files being scanned could
# have the same basename, so this path won't be unique, but the
# checks don't run concurrently so this shouldn't cause any
# problems.
pyc_path = os.path.join(temp_dir, os.path.basename(filename) + ".pyc")
try:
if USING_PYTHON2:
filename = filename.encode(sys.getfilesystemencoding())
py_compile.compile(filename, cfile=pyc_path, doraise=True)
finally:
try:
os.unlink(pyc_path)
except OSError:
pass
def check_bash(filename):
if platform.system() == 'Windows':
logging.warn(
'Skipping bash syntax check due to unavailability of bash on Windows.')
else:
subprocess.check_output(["/bin/bash", "-n", filename], stderr=subprocess.STDOUT)
if override_lang == 'python2.7':
checker_fn = check_python
elif override_lang == 'bash':
checker_fn = check_bash
elif filename.endswith('.py'):
checker_fn = check_python
elif filename.endswith('.sh'):
checker_fn = check_bash
else:
# Ignore other kinds of files.
return
# Do a test read of the file to catch errors like the file not
# existing or not being readable.
open(filename)
try:
checker_fn(filename)
except subprocess.CalledProcessError as e:
print(filename + " has a syntax error! Interpreter output:", file=sys.stderr)
for line in e.output.strip("\n").split("\n"):
print(" " + line.rstrip("\n"), file=sys.stderr)
if enforce:
raise DXSyntaxError(filename + " has a syntax error")
except py_compile.PyCompileError as e:
print(filename + " has a syntax error! Interpreter output:", file=sys.stderr)
print(" " + e.msg.strip(), file=sys.stderr)
if enforce:
raise DXSyntaxError(e.msg.strip()) | python | def _check_file_syntax(filename, temp_dir, override_lang=None, enforce=True):
def check_python(filename):
# Generate a semi-recognizable name to write the pyc to. Of
# course it's possible that different files being scanned could
# have the same basename, so this path won't be unique, but the
# checks don't run concurrently so this shouldn't cause any
# problems.
pyc_path = os.path.join(temp_dir, os.path.basename(filename) + ".pyc")
try:
if USING_PYTHON2:
filename = filename.encode(sys.getfilesystemencoding())
py_compile.compile(filename, cfile=pyc_path, doraise=True)
finally:
try:
os.unlink(pyc_path)
except OSError:
pass
def check_bash(filename):
if platform.system() == 'Windows':
logging.warn(
'Skipping bash syntax check due to unavailability of bash on Windows.')
else:
subprocess.check_output(["/bin/bash", "-n", filename], stderr=subprocess.STDOUT)
if override_lang == 'python2.7':
checker_fn = check_python
elif override_lang == 'bash':
checker_fn = check_bash
elif filename.endswith('.py'):
checker_fn = check_python
elif filename.endswith('.sh'):
checker_fn = check_bash
else:
# Ignore other kinds of files.
return
# Do a test read of the file to catch errors like the file not
# existing or not being readable.
open(filename)
try:
checker_fn(filename)
except subprocess.CalledProcessError as e:
print(filename + " has a syntax error! Interpreter output:", file=sys.stderr)
for line in e.output.strip("\n").split("\n"):
print(" " + line.rstrip("\n"), file=sys.stderr)
if enforce:
raise DXSyntaxError(filename + " has a syntax error")
except py_compile.PyCompileError as e:
print(filename + " has a syntax error! Interpreter output:", file=sys.stderr)
print(" " + e.msg.strip(), file=sys.stderr)
if enforce:
raise DXSyntaxError(e.msg.strip()) | [
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12,032 | dnanexus/dx-toolkit | src/python/dxpy/scripts/dx_build_app.py | _parse_app_spec | def _parse_app_spec(src_dir):
"""Returns the parsed contents of dxapp.json.
Raises either AppBuilderException or a parser error (exit codes 3 or
2 respectively) if this cannot be done.
"""
if not os.path.isdir(src_dir):
parser.error("%s is not a directory" % src_dir)
if not os.path.exists(os.path.join(src_dir, "dxapp.json")):
raise dxpy.app_builder.AppBuilderException("Directory %s does not contain dxapp.json: not a valid DNAnexus app source directory" % src_dir)
with open(os.path.join(src_dir, "dxapp.json")) as app_desc:
try:
return json_load_raise_on_duplicates(app_desc)
except Exception as e:
raise dxpy.app_builder.AppBuilderException("Could not parse dxapp.json file as JSON: " + str(e.args)) | python | def _parse_app_spec(src_dir):
if not os.path.isdir(src_dir):
parser.error("%s is not a directory" % src_dir)
if not os.path.exists(os.path.join(src_dir, "dxapp.json")):
raise dxpy.app_builder.AppBuilderException("Directory %s does not contain dxapp.json: not a valid DNAnexus app source directory" % src_dir)
with open(os.path.join(src_dir, "dxapp.json")) as app_desc:
try:
return json_load_raise_on_duplicates(app_desc)
except Exception as e:
raise dxpy.app_builder.AppBuilderException("Could not parse dxapp.json file as JSON: " + str(e.args)) | [
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] | 74befb53ad90fcf902d8983ae6d74580f402d619 | https://github.com/dnanexus/dx-toolkit/blob/74befb53ad90fcf902d8983ae6d74580f402d619/src/python/dxpy/scripts/dx_build_app.py#L454-L468 |
12,033 | dnanexus/dx-toolkit | src/python/dxpy/bindings/dxapp.py | DXApp.install | def install(self, **kwargs):
"""
Installs the app in the current user's account.
"""
if self._dxid is not None:
return dxpy.api.app_install(self._dxid, **kwargs)
else:
return dxpy.api.app_install('app-' + self._name, alias=self._alias, **kwargs) | python | def install(self, **kwargs):
if self._dxid is not None:
return dxpy.api.app_install(self._dxid, **kwargs)
else:
return dxpy.api.app_install('app-' + self._name, alias=self._alias, **kwargs) | [
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12,034 | dnanexus/dx-toolkit | src/python/dxpy/bindings/dxapp.py | DXApp.uninstall | def uninstall(self, **kwargs):
"""
Uninstalls the app from the current user's account.
"""
if self._dxid is not None:
return dxpy.api.app_uninstall(self._dxid, **kwargs)
else:
return dxpy.api.app_uninstall('app-' + self._name, alias=self._alias, **kwargs) | python | def uninstall(self, **kwargs):
if self._dxid is not None:
return dxpy.api.app_uninstall(self._dxid, **kwargs)
else:
return dxpy.api.app_uninstall('app-' + self._name, alias=self._alias, **kwargs) | [
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12,035 | dnanexus/dx-toolkit | src/python/dxpy/bindings/dxapp.py | DXApp.delete | def delete(self, **kwargs):
"""
Removes this app object from the platform.
The current user must be a developer of the app.
"""
if self._dxid is not None:
return dxpy.api.app_delete(self._dxid, **kwargs)
else:
return dxpy.api.app_delete('app-' + self._name, alias=self._alias, **kwargs) | python | def delete(self, **kwargs):
if self._dxid is not None:
return dxpy.api.app_delete(self._dxid, **kwargs)
else:
return dxpy.api.app_delete('app-' + self._name, alias=self._alias, **kwargs) | [
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12,036 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _version_exists | def _version_exists(json_spec, name=None, version=None):
"""
Returns True if a global workflow with the given name and version
already exists in the platform and the user has developer rights
to the workflow. "name" and "version" can be passed if we already
made a "describe" API call on the global workflow and so know the
requested name and version already exists.
"""
requested_name = json_spec['name']
requested_version = json_spec['version']
if requested_name == name and requested_version == version:
return True
else:
try:
desc_output = dxpy.api.global_workflow_describe('globalworkflow-' + json_spec['name'],
alias=json_spec['version'],
input_params={"fields": {"name": True,
"version": True}})
return desc_output['name'] == json_spec['name'] and desc_output['version'] == json_spec['version']
except dxpy.exceptions.DXAPIError:
return False
except:
raise | python | def _version_exists(json_spec, name=None, version=None):
requested_name = json_spec['name']
requested_version = json_spec['version']
if requested_name == name and requested_version == version:
return True
else:
try:
desc_output = dxpy.api.global_workflow_describe('globalworkflow-' + json_spec['name'],
alias=json_spec['version'],
input_params={"fields": {"name": True,
"version": True}})
return desc_output['name'] == json_spec['name'] and desc_output['version'] == json_spec['version']
except dxpy.exceptions.DXAPIError:
return False
except:
raise | [
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12,037 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _get_validated_stages | def _get_validated_stages(stages):
"""
Validates stages of the workflow as a list of dictionaries.
"""
if not isinstance(stages, list):
raise WorkflowBuilderException("Stages must be specified as a list of dictionaries")
validated_stages = []
for index, stage in enumerate(stages):
validated_stages.append(_get_validated_stage(stage, index))
return validated_stages | python | def _get_validated_stages(stages):
if not isinstance(stages, list):
raise WorkflowBuilderException("Stages must be specified as a list of dictionaries")
validated_stages = []
for index, stage in enumerate(stages):
validated_stages.append(_get_validated_stage(stage, index))
return validated_stages | [
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12,038 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _validate_json_for_regular_workflow | def _validate_json_for_regular_workflow(json_spec, args):
"""
Validates fields used only for building a regular, project-based workflow.
"""
validated = {}
override_project_id, override_folder, override_workflow_name = \
dxpy.executable_builder.get_parsed_destination(args.destination)
validated['project'] = _get_destination_project(json_spec, args, override_project_id)
validated['folder'] = _get_destination_folder(json_spec, override_folder)
workflow_name = _get_workflow_name(json_spec, override_workflow_name)
if not workflow_name:
print('Warning: workflow name is not specified')
else:
validated['name'] = workflow_name
return validated | python | def _validate_json_for_regular_workflow(json_spec, args):
validated = {}
override_project_id, override_folder, override_workflow_name = \
dxpy.executable_builder.get_parsed_destination(args.destination)
validated['project'] = _get_destination_project(json_spec, args, override_project_id)
validated['folder'] = _get_destination_folder(json_spec, override_folder)
workflow_name = _get_workflow_name(json_spec, override_workflow_name)
if not workflow_name:
print('Warning: workflow name is not specified')
else:
validated['name'] = workflow_name
return validated | [
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12,039 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _validate_json_for_global_workflow | def _validate_json_for_global_workflow(json_spec, args):
"""
Validates fields used for building a global workflow.
Since building a global workflow is done after all the underlying workflows
are built, which may be time-consuming, we validate as much as possible here.
"""
# TODO: verify the billTo can build the workflow
# TODO: if the global workflow build fails add an option to interactively change billto
# TODO: (or other simple fields) instead of failing altogether
# TODO: get a confirmation before building a workflow that may be costly
if 'name' not in json_spec:
raise WorkflowBuilderException(
"dxworkflow.json contains no 'name' field, but it is required to build a global workflow")
if not dxpy.executable_builder.GLOBAL_EXEC_NAME_RE.match(json_spec['name']):
raise WorkflowBuilderException(
"The name of your workflow must match /^[a-zA-Z0-9._-]+$/")
if json_spec['name'] != json_spec['name'].lower():
logger.warn('workflow name "{}" should be all lowercase'.format(json_spec['name']))
if 'version' not in json_spec:
raise WorkflowBuilderException(
"dxworkflow.json contains no 'version' field, but it is required to build a global workflow")
if not dxpy.executable_builder.GLOBAL_EXEC_VERSION_RE.match(json_spec['version']):
logger.warn('"version" {} should be semver compliant (e.g. of the form X.Y.Z)'.format(json_spec['version']))
if 'details' in json_spec:
if not isinstance(json_spec['details'], dict):
raise WorkflowBuilderException(
'The field "details" must be a dictionary')
if 'regionalOptions' in json_spec:
if not (isinstance(json_spec['regionalOptions'], dict)
and json_spec['regionalOptions']
and all([isinstance(i, dict) for i in json_spec['regionalOptions'].values()])):
raise WorkflowBuilderException(
'The field "regionalOptions" must be a non-empty dictionary whose values are dictionaries')
if args.bill_to:
json_spec["billTo"] = args.bill_to | python | def _validate_json_for_global_workflow(json_spec, args):
# TODO: verify the billTo can build the workflow
# TODO: if the global workflow build fails add an option to interactively change billto
# TODO: (or other simple fields) instead of failing altogether
# TODO: get a confirmation before building a workflow that may be costly
if 'name' not in json_spec:
raise WorkflowBuilderException(
"dxworkflow.json contains no 'name' field, but it is required to build a global workflow")
if not dxpy.executable_builder.GLOBAL_EXEC_NAME_RE.match(json_spec['name']):
raise WorkflowBuilderException(
"The name of your workflow must match /^[a-zA-Z0-9._-]+$/")
if json_spec['name'] != json_spec['name'].lower():
logger.warn('workflow name "{}" should be all lowercase'.format(json_spec['name']))
if 'version' not in json_spec:
raise WorkflowBuilderException(
"dxworkflow.json contains no 'version' field, but it is required to build a global workflow")
if not dxpy.executable_builder.GLOBAL_EXEC_VERSION_RE.match(json_spec['version']):
logger.warn('"version" {} should be semver compliant (e.g. of the form X.Y.Z)'.format(json_spec['version']))
if 'details' in json_spec:
if not isinstance(json_spec['details'], dict):
raise WorkflowBuilderException(
'The field "details" must be a dictionary')
if 'regionalOptions' in json_spec:
if not (isinstance(json_spec['regionalOptions'], dict)
and json_spec['regionalOptions']
and all([isinstance(i, dict) for i in json_spec['regionalOptions'].values()])):
raise WorkflowBuilderException(
'The field "regionalOptions" must be a non-empty dictionary whose values are dictionaries')
if args.bill_to:
json_spec["billTo"] = args.bill_to | [
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12,040 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _create_temporary_projects | def _create_temporary_projects(enabled_regions, args):
"""
Creates a temporary project needed to build an underlying workflow
for a global workflow. Returns a dictionary with region names as keys
and project IDs as values
The regions in which projects will be created can be:
i. regions specified in dxworkflow.json "regionalOptions"
ii. regions specified as an argument to "dx build"
iii. current context project, if None of the above are set
If both args and dxworkflow.json specify regions, they must match.
"""
# Create one temp project in each region
projects_by_region = {} # Project IDs by region
for region in enabled_regions:
try:
project_input = {"name": "Temporary build project for dx build global workflow",
"region": region}
if args.bill_to:
project_input["billTo"] = args.bill_to
temp_project = dxpy.api.project_new(project_input)["id"]
projects_by_region[region] = temp_project
logger.debug("Created temporary project {} to build in".format(temp_project))
except:
# Clean up any temp projects that might have been created
if projects_by_region:
dxpy.executable_builder.delete_temporary_projects(projects_by_region.values())
err_exit()
return projects_by_region | python | def _create_temporary_projects(enabled_regions, args):
# Create one temp project in each region
projects_by_region = {} # Project IDs by region
for region in enabled_regions:
try:
project_input = {"name": "Temporary build project for dx build global workflow",
"region": region}
if args.bill_to:
project_input["billTo"] = args.bill_to
temp_project = dxpy.api.project_new(project_input)["id"]
projects_by_region[region] = temp_project
logger.debug("Created temporary project {} to build in".format(temp_project))
except:
# Clean up any temp projects that might have been created
if projects_by_region:
dxpy.executable_builder.delete_temporary_projects(projects_by_region.values())
err_exit()
return projects_by_region | [
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12,041 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _build_global_workflow | def _build_global_workflow(json_spec, args):
"""
Creates a workflow in a temporary project for each enabled region
and builds a global workflow on the platform based on these workflows.
"""
# First determine in which regions the global workflow needs to be available
enabled_regions = _get_validated_enabled_regions(json_spec, args.region)
# Verify all the stages are also enabled in these regions
# TODO: Add support for dx building multi-region global workflows with applets
_assert_executable_regions_match(enabled_regions, json_spec)
workflows_by_region, projects_by_region = {}, {} # IDs by region
try:
# prepare "regionalOptions" field for the globalworkflow/new input
workflows_by_region, projects_by_region = \
_build_underlying_workflows(enabled_regions, json_spec, args)
regional_options = {}
for region, workflow_id in workflows_by_region.items():
regional_options[region] = {'workflow': workflow_id}
json_spec.update({'regionalOptions': regional_options})
# leave only fields that are actually used to build the workflow
gwf_provided_keys = GLOBALWF_SUPPORTED_KEYS.intersection(set(json_spec.keys()))
gwf_final_json = dict((k, v) for k, v in json_spec.items() if k in gwf_provided_keys)
# we don't want to print the whole documentation to the screen so we'll remove these fields
print_spec = copy.deepcopy(gwf_final_json)
if "description" in gwf_final_json:
del print_spec["description"]
if "developerNotes" in gwf_final_json:
del print_spec["developerNotes"]
logger.info("Will create global workflow with spec: {}".format(json.dumps(print_spec)))
# Create a new global workflow version on the platform
global_workflow_id = dxpy.api.global_workflow_new(gwf_final_json)["id"]
logger.info("Uploaded global workflow {n}/{v} successfully".format(n=gwf_final_json["name"],
v=gwf_final_json["version"]))
logger.info("You can publish this workflow with:")
logger.info(" dx publish {n}/{v}".format(n=gwf_final_json["name"],
v=gwf_final_json["version"]))
finally:
# Clean up
if projects_by_region:
dxpy.executable_builder.delete_temporary_projects(projects_by_region.values())
# Set any additional fields on the created workflow
try:
_set_categories_on_workflow(global_workflow_id, gwf_final_json.get("categories", []))
except:
logger.warn(
"The workflow {n}/{v} was created but setting categories failed".format(n=gwf_final_json['name'],
v=gwf_final_json['version']))
raise
return global_workflow_id | python | def _build_global_workflow(json_spec, args):
# First determine in which regions the global workflow needs to be available
enabled_regions = _get_validated_enabled_regions(json_spec, args.region)
# Verify all the stages are also enabled in these regions
# TODO: Add support for dx building multi-region global workflows with applets
_assert_executable_regions_match(enabled_regions, json_spec)
workflows_by_region, projects_by_region = {}, {} # IDs by region
try:
# prepare "regionalOptions" field for the globalworkflow/new input
workflows_by_region, projects_by_region = \
_build_underlying_workflows(enabled_regions, json_spec, args)
regional_options = {}
for region, workflow_id in workflows_by_region.items():
regional_options[region] = {'workflow': workflow_id}
json_spec.update({'regionalOptions': regional_options})
# leave only fields that are actually used to build the workflow
gwf_provided_keys = GLOBALWF_SUPPORTED_KEYS.intersection(set(json_spec.keys()))
gwf_final_json = dict((k, v) for k, v in json_spec.items() if k in gwf_provided_keys)
# we don't want to print the whole documentation to the screen so we'll remove these fields
print_spec = copy.deepcopy(gwf_final_json)
if "description" in gwf_final_json:
del print_spec["description"]
if "developerNotes" in gwf_final_json:
del print_spec["developerNotes"]
logger.info("Will create global workflow with spec: {}".format(json.dumps(print_spec)))
# Create a new global workflow version on the platform
global_workflow_id = dxpy.api.global_workflow_new(gwf_final_json)["id"]
logger.info("Uploaded global workflow {n}/{v} successfully".format(n=gwf_final_json["name"],
v=gwf_final_json["version"]))
logger.info("You can publish this workflow with:")
logger.info(" dx publish {n}/{v}".format(n=gwf_final_json["name"],
v=gwf_final_json["version"]))
finally:
# Clean up
if projects_by_region:
dxpy.executable_builder.delete_temporary_projects(projects_by_region.values())
# Set any additional fields on the created workflow
try:
_set_categories_on_workflow(global_workflow_id, gwf_final_json.get("categories", []))
except:
logger.warn(
"The workflow {n}/{v} was created but setting categories failed".format(n=gwf_final_json['name'],
v=gwf_final_json['version']))
raise
return global_workflow_id | [
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12,042 | dnanexus/dx-toolkit | src/python/dxpy/workflow_builder.py | _build_or_update_workflow | def _build_or_update_workflow(json_spec, args):
"""
Creates or updates a workflow on the platform.
Returns the workflow ID, or None if the workflow cannot be created.
"""
try:
if args.mode == 'workflow':
json_spec = _get_validated_json(json_spec, args)
workflow_id = _build_regular_workflow(json_spec)
elif args.mode == 'globalworkflow':
# Verify if the global workflow already exists and if the user has developer rights to it
# If the global workflow name doesn't exist, the user is free to build it
# If the name does exist two things can be done:
# * either update the requested version, if this version already exists
# * or create the version if it doesn't exist
existing_workflow = dxpy.executable_builder.verify_developer_rights('globalworkflow-' + json_spec['name'])
if existing_workflow and _version_exists(json_spec,
existing_workflow.name,
existing_workflow.version):
workflow_id = _update_global_workflow(json_spec, args, existing_workflow.id)
else:
json_spec = _get_validated_json(json_spec, args)
workflow_id = _build_global_workflow(json_spec, args)
else:
raise WorkflowBuilderException("Unrecognized workflow type: {}".format(args.mode))
except dxpy.exceptions.DXAPIError as e:
raise e
return workflow_id | python | def _build_or_update_workflow(json_spec, args):
try:
if args.mode == 'workflow':
json_spec = _get_validated_json(json_spec, args)
workflow_id = _build_regular_workflow(json_spec)
elif args.mode == 'globalworkflow':
# Verify if the global workflow already exists and if the user has developer rights to it
# If the global workflow name doesn't exist, the user is free to build it
# If the name does exist two things can be done:
# * either update the requested version, if this version already exists
# * or create the version if it doesn't exist
existing_workflow = dxpy.executable_builder.verify_developer_rights('globalworkflow-' + json_spec['name'])
if existing_workflow and _version_exists(json_spec,
existing_workflow.name,
existing_workflow.version):
workflow_id = _update_global_workflow(json_spec, args, existing_workflow.id)
else:
json_spec = _get_validated_json(json_spec, args)
workflow_id = _build_global_workflow(json_spec, args)
else:
raise WorkflowBuilderException("Unrecognized workflow type: {}".format(args.mode))
except dxpy.exceptions.DXAPIError as e:
raise e
return workflow_id | [
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12,043 | dnanexus/dx-toolkit | src/python/dxpy/bindings/dxfile.py | _readable_part_size | def _readable_part_size(num_bytes):
"Returns the file size in readable form."
B = num_bytes
KB = float(1024)
MB = float(KB * 1024)
GB = float(MB * 1024)
TB = float(GB * 1024)
if B < KB:
return '{0} {1}'.format(B, 'bytes' if B != 1 else 'byte')
elif KB <= B < MB:
return '{0:.2f} KiB'.format(B/KB)
elif MB <= B < GB:
return '{0:.2f} MiB'.format(B/MB)
elif GB <= B < TB:
return '{0:.2f} GiB'.format(B/GB)
elif TB <= B:
return '{0:.2f} TiB'.format(B/TB) | python | def _readable_part_size(num_bytes):
"Returns the file size in readable form."
B = num_bytes
KB = float(1024)
MB = float(KB * 1024)
GB = float(MB * 1024)
TB = float(GB * 1024)
if B < KB:
return '{0} {1}'.format(B, 'bytes' if B != 1 else 'byte')
elif KB <= B < MB:
return '{0:.2f} KiB'.format(B/KB)
elif MB <= B < GB:
return '{0:.2f} MiB'.format(B/MB)
elif GB <= B < TB:
return '{0:.2f} GiB'.format(B/GB)
elif TB <= B:
return '{0:.2f} TiB'.format(B/TB) | [
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12,044 | dnanexus/dx-toolkit | src/python/dxpy/bindings/dxfile.py | DXFile.flush | def flush(self, multithread=True, **kwargs):
'''
Flushes the internal write buffer.
'''
if self._write_buf.tell() > 0:
data = self._write_buf.getvalue()
self._write_buf = BytesIO()
if multithread:
self._async_upload_part_request(data, index=self._cur_part, **kwargs)
else:
self.upload_part(data, self._cur_part, **kwargs)
self._cur_part += 1
if len(self._http_threadpool_futures) > 0:
dxpy.utils.wait_for_all_futures(self._http_threadpool_futures)
try:
for future in self._http_threadpool_futures:
if future.exception() != None:
raise future.exception()
finally:
self._http_threadpool_futures = set() | python | def flush(self, multithread=True, **kwargs):
'''
Flushes the internal write buffer.
'''
if self._write_buf.tell() > 0:
data = self._write_buf.getvalue()
self._write_buf = BytesIO()
if multithread:
self._async_upload_part_request(data, index=self._cur_part, **kwargs)
else:
self.upload_part(data, self._cur_part, **kwargs)
self._cur_part += 1
if len(self._http_threadpool_futures) > 0:
dxpy.utils.wait_for_all_futures(self._http_threadpool_futures)
try:
for future in self._http_threadpool_futures:
if future.exception() != None:
raise future.exception()
finally:
self._http_threadpool_futures = set() | [
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12,045 | dnanexus/dx-toolkit | src/python/dxpy/ssh_tunnel_app_support.py | setup_ssh_tunnel | def setup_ssh_tunnel(job_id, local_port, remote_port):
"""
Setup an ssh tunnel to the given job-id. This will establish
the port over the given local_port to the given remote_port
and then exit, keeping the tunnel in place until the job is
terminated.
"""
cmd = ['dx', 'ssh', '--suppress-running-check', job_id, '-o', 'StrictHostKeyChecking no']
cmd += ['-f', '-L', '{0}:localhost:{1}'.format(local_port, remote_port), '-N']
subprocess.check_call(cmd) | python | def setup_ssh_tunnel(job_id, local_port, remote_port):
cmd = ['dx', 'ssh', '--suppress-running-check', job_id, '-o', 'StrictHostKeyChecking no']
cmd += ['-f', '-L', '{0}:localhost:{1}'.format(local_port, remote_port), '-N']
subprocess.check_call(cmd) | [
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12,046 | dnanexus/dx-toolkit | src/python/dxpy/ssh_tunnel_app_support.py | poll_for_server_running | def poll_for_server_running(job_id):
"""
Poll for the job to start running and post the SERVER_READY_TAG.
"""
sys.stdout.write('Waiting for server in {0} to initialize ...'.format(job_id))
sys.stdout.flush()
desc = dxpy.describe(job_id)
# Keep checking until the server has begun or it has failed.
while(SERVER_READY_TAG not in desc['tags'] and desc['state'] != 'failed'):
time.sleep(SLEEP_PERIOD)
sys.stdout.write('.')
sys.stdout.flush()
desc = dxpy.describe(job_id)
# If the server job failed, provide friendly advice.
if desc['state'] == 'failed':
msg = RED('Error:') + ' Server failed to run.\n'
msg += 'You may want to check the job logs by running:'
msg += BOLD('dx watch {0}'.format(job_id))
err_exit(msg) | python | def poll_for_server_running(job_id):
sys.stdout.write('Waiting for server in {0} to initialize ...'.format(job_id))
sys.stdout.flush()
desc = dxpy.describe(job_id)
# Keep checking until the server has begun or it has failed.
while(SERVER_READY_TAG not in desc['tags'] and desc['state'] != 'failed'):
time.sleep(SLEEP_PERIOD)
sys.stdout.write('.')
sys.stdout.flush()
desc = dxpy.describe(job_id)
# If the server job failed, provide friendly advice.
if desc['state'] == 'failed':
msg = RED('Error:') + ' Server failed to run.\n'
msg += 'You may want to check the job logs by running:'
msg += BOLD('dx watch {0}'.format(job_id))
err_exit(msg) | [
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12,047 | dnanexus/dx-toolkit | src/python/dxpy/ssh_tunnel_app_support.py | multi_platform_open | def multi_platform_open(cmd):
"""
Take the given command and use the OS to automatically open the appropriate
resource. For instance, if a URL is provided, this will have the OS automatically
open the URL in the default web browser.
"""
if platform == "linux" or platform == "linux2":
cmd = ['xdg-open', cmd]
elif platform == "darwin":
cmd = ['open', cmd]
elif platform == "win32":
cmd = ['start', cmd]
subprocess.check_call(cmd) | python | def multi_platform_open(cmd):
if platform == "linux" or platform == "linux2":
cmd = ['xdg-open', cmd]
elif platform == "darwin":
cmd = ['open', cmd]
elif platform == "win32":
cmd = ['start', cmd]
subprocess.check_call(cmd) | [
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12,048 | dnanexus/dx-toolkit | src/python/dxpy/ssh_tunnel_app_support.py | get_notebook_app_versions | def get_notebook_app_versions():
"""
Get the valid version numbers of the notebook app.
"""
notebook_apps = dxpy.find_apps(name=NOTEBOOK_APP, all_versions=True)
versions = [str(dxpy.describe(app['id'])['version']) for app in notebook_apps]
return versions | python | def get_notebook_app_versions():
notebook_apps = dxpy.find_apps(name=NOTEBOOK_APP, all_versions=True)
versions = [str(dxpy.describe(app['id'])['version']) for app in notebook_apps]
return versions | [
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12,049 | dnanexus/dx-toolkit | src/python/dxpy/ssh_tunnel_app_support.py | run_notebook | def run_notebook(args, ssh_config_check):
"""
Launch the notebook server.
"""
# Check that ssh is setup. Currently notebooks require ssh for tunelling.
ssh_config_check()
if args.only_check_config:
return
# If the user requested a specific version of the notebook server,
# get the executable id.
if args.version is not None:
executable = get_app_from_path('app-{0}/{1}'.format(NOTEBOOK_APP, args.version))
if executable is not None and 'id' in executable:
executable = executable['id']
else:
msg = RED('Warning:') + ' Invalid notebook version: {0}\nValid versions are: '.format(args.version)
msg += BOLD('{0}'.format(str(get_notebook_app_versions())))
err_exit(msg)
else:
executable = 'app-{0}'.format(NOTEBOOK_APP)
# Compose the command to launch the notebook
cmd = ['dx', 'run', executable, '-inotebook_type={0}'.format(args.notebook_type)]
cmd += ['-iinput_files={0}'.format(f) for f in args.notebook_files]
cmd += ['-itimeout={0}'.format(args.timeout), '-y', '--brief', '--allow-ssh', '--instance-type', args.instance_type]
if args.spark:
cmd += ['-iinstall_spark=true']
if args.snapshot:
cmd += ['-isnapshot={0}'.format(args.snapshot)]
job_id = subprocess.check_output(cmd).strip()
poll_for_server_running(job_id)
if args.notebook_type in {'jupyter', 'jupyter_lab', 'jupyter_notebook'}:
remote_port = 8888
setup_ssh_tunnel(job_id, args.port, remote_port)
if args.open_server:
multi_platform_open('http://localhost:{0}'.format(args.port))
print('A web browser should have opened to connect you to your notebook.')
print('If no browser appears, or if you need to reopen a browser at any point, you should be able to point your browser to http://localhost:{0}'.format(args.port)) | python | def run_notebook(args, ssh_config_check):
# Check that ssh is setup. Currently notebooks require ssh for tunelling.
ssh_config_check()
if args.only_check_config:
return
# If the user requested a specific version of the notebook server,
# get the executable id.
if args.version is not None:
executable = get_app_from_path('app-{0}/{1}'.format(NOTEBOOK_APP, args.version))
if executable is not None and 'id' in executable:
executable = executable['id']
else:
msg = RED('Warning:') + ' Invalid notebook version: {0}\nValid versions are: '.format(args.version)
msg += BOLD('{0}'.format(str(get_notebook_app_versions())))
err_exit(msg)
else:
executable = 'app-{0}'.format(NOTEBOOK_APP)
# Compose the command to launch the notebook
cmd = ['dx', 'run', executable, '-inotebook_type={0}'.format(args.notebook_type)]
cmd += ['-iinput_files={0}'.format(f) for f in args.notebook_files]
cmd += ['-itimeout={0}'.format(args.timeout), '-y', '--brief', '--allow-ssh', '--instance-type', args.instance_type]
if args.spark:
cmd += ['-iinstall_spark=true']
if args.snapshot:
cmd += ['-isnapshot={0}'.format(args.snapshot)]
job_id = subprocess.check_output(cmd).strip()
poll_for_server_running(job_id)
if args.notebook_type in {'jupyter', 'jupyter_lab', 'jupyter_notebook'}:
remote_port = 8888
setup_ssh_tunnel(job_id, args.port, remote_port)
if args.open_server:
multi_platform_open('http://localhost:{0}'.format(args.port))
print('A web browser should have opened to connect you to your notebook.')
print('If no browser appears, or if you need to reopen a browser at any point, you should be able to point your browser to http://localhost:{0}'.format(args.port)) | [
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12,050 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/model_datastore_input_reader.py | ModelDatastoreInputReader._validate_filters | def _validate_filters(cls, filters, model_class):
"""Validate user supplied filters.
Validate filters are on existing properties and filter values
have valid semantics.
Args:
filters: user supplied filters. Each filter should be a list or tuple of
format (<property_name_as_str>, <query_operator_as_str>,
<value_of_certain_type>). Value type is up to the property's type.
model_class: the db.Model class for the entity type to apply filters on.
Raises:
BadReaderParamsError: if any filter is invalid in any way.
"""
if not filters:
return
properties = model_class.properties()
for f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
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prop, model_class.kind())
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop].validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | python | def _validate_filters(cls, filters, model_class):
if not filters:
return
properties = model_class.properties()
for f in filters:
prop, _, val = f
if prop not in properties:
raise errors.BadReaderParamsError(
"Property %s is not defined for entity type %s",
prop, model_class.kind())
# Validate the value of each filter. We need to know filters have
# valid value to carry out splits.
try:
properties[prop].validate(val)
except db.BadValueError, e:
raise errors.BadReaderParamsError(e) | [
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Args:
filters: user supplied filters. Each filter should be a list or tuple of
format (<property_name_as_str>, <query_operator_as_str>,
<value_of_certain_type>). Value type is up to the property's type.
model_class: the db.Model class for the entity type to apply filters on.
Raises:
BadReaderParamsError: if any filter is invalid in any way. | [
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] | 2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6 | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/api/map_job/model_datastore_input_reader.py#L118-L150 |
12,051 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _normalize_entity | def _normalize_entity(value):
"""Return an entity from an entity or model instance."""
if ndb is not None and isinstance(value, ndb.Model):
return None
if getattr(value, "_populate_internal_entity", None):
return value._populate_internal_entity()
return value | python | def _normalize_entity(value):
if ndb is not None and isinstance(value, ndb.Model):
return None
if getattr(value, "_populate_internal_entity", None):
return value._populate_internal_entity()
return value | [
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12,052 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _normalize_key | def _normalize_key(value):
"""Return a key from an entity, model instance, key, or key string."""
if ndb is not None and isinstance(value, (ndb.Model, ndb.Key)):
return None
if getattr(value, "key", None):
return value.key()
elif isinstance(value, basestring):
return datastore.Key(value)
else:
return value | python | def _normalize_key(value):
if ndb is not None and isinstance(value, (ndb.Model, ndb.Key)):
return None
if getattr(value, "key", None):
return value.key()
elif isinstance(value, basestring):
return datastore.Key(value)
else:
return value | [
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12,053 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _ItemList.append | def append(self, item):
"""Add new item to the list.
If needed, append will first flush existing items and clear existing items.
Args:
item: an item to add to the list.
"""
if self.should_flush():
self.flush()
self.items.append(item) | python | def append(self, item):
if self.should_flush():
self.flush()
self.items.append(item) | [
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12,054 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _ItemList.flush | def flush(self):
"""Force a flush."""
if not self.items:
return
retry = 0
options = {"deadline": DATASTORE_DEADLINE}
while retry <= self.__timeout_retries:
try:
self.__flush_function(self.items, options)
self.clear()
break
except db.Timeout, e:
logging.warning(e)
logging.warning("Flushing '%s' timed out. Will retry for the %s time.",
self, retry)
retry += 1
options["deadline"] *= 2
except apiproxy_errors.RequestTooLargeError:
self._log_largest_items()
raise
else:
raise | python | def flush(self):
if not self.items:
return
retry = 0
options = {"deadline": DATASTORE_DEADLINE}
while retry <= self.__timeout_retries:
try:
self.__flush_function(self.items, options)
self.clear()
break
except db.Timeout, e:
logging.warning(e)
logging.warning("Flushing '%s' timed out. Will retry for the %s time.",
self, retry)
retry += 1
options["deadline"] *= 2
except apiproxy_errors.RequestTooLargeError:
self._log_largest_items()
raise
else:
raise | [
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12,055 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.put | def put(self, entity):
"""Registers entity to put to datastore.
Args:
entity: an entity or model instance to put.
"""
actual_entity = _normalize_entity(entity)
if actual_entity is None:
return self.ndb_put(entity)
self.puts.append(actual_entity) | python | def put(self, entity):
actual_entity = _normalize_entity(entity)
if actual_entity is None:
return self.ndb_put(entity)
self.puts.append(actual_entity) | [
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12,056 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool.delete | def delete(self, entity):
"""Registers entity to delete from datastore.
Args:
entity: an entity, model instance, or key to delete.
"""
key = _normalize_key(entity)
if key is None:
return self.ndb_delete(entity)
self.deletes.append(key) | python | def delete(self, entity):
key = _normalize_key(entity)
if key is None:
return self.ndb_delete(entity)
self.deletes.append(key) | [
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12,057 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_puts | def _flush_puts(self, items, options):
"""Flush all puts to datastore."""
datastore.Put(items, config=self._create_config(options)) | python | def _flush_puts(self, items, options):
datastore.Put(items, config=self._create_config(options)) | [
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12,058 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._flush_ndb_puts | def _flush_ndb_puts(self, items, options):
"""Flush all NDB puts to datastore."""
assert ndb is not None
ndb.put_multi(items, config=self._create_config(options)) | python | def _flush_ndb_puts(self, items, options):
assert ndb is not None
ndb.put_multi(items, config=self._create_config(options)) | [
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12,059 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/context.py | _MutationPool._create_config | def _create_config(self, options):
"""Creates datastore Config.
Returns:
A datastore_rpc.Configuration instance.
"""
return datastore.CreateConfig(deadline=options["deadline"],
force_writes=self.force_writes) | python | def _create_config(self, options):
return datastore.CreateConfig(deadline=options["deadline"],
force_writes=self.force_writes) | [
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12,060 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | AbstractKeyRangeIterator.to_json | def to_json(self):
"""Serializes all states into json form.
Returns:
all states in json-compatible map.
"""
cursor = self._get_cursor()
cursor_object = False
if cursor and isinstance(cursor, datastore_query.Cursor):
cursor = cursor.to_websafe_string()
cursor_object = True
return {"key_range": self._key_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"cursor_object": cursor_object} | python | def to_json(self):
cursor = self._get_cursor()
cursor_object = False
if cursor and isinstance(cursor, datastore_query.Cursor):
cursor = cursor.to_websafe_string()
cursor_object = True
return {"key_range": self._key_range.to_json(),
"query_spec": self._query_spec.to_json(),
"cursor": cursor,
"cursor_object": cursor_object} | [
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12,061 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/datastore_range_iterators.py | AbstractKeyRangeIterator.from_json | def from_json(cls, json):
"""Reverse of to_json."""
obj = cls(key_range.KeyRange.from_json(json["key_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | python | def from_json(cls, json):
obj = cls(key_range.KeyRange.from_json(json["key_range"]),
model.QuerySpec.from_json(json["query_spec"]))
cursor = json["cursor"]
# lint bug. Class method can access protected fields.
# pylint: disable=protected-access
if cursor and json["cursor_object"]:
obj._cursor = datastore_query.Cursor.from_websafe_string(cursor)
else:
obj._cursor = cursor
return obj | [
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12,062 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | find_mapreduce_yaml | def find_mapreduce_yaml(status_file=__file__):
"""Traverse directory trees to find mapreduce.yaml file.
Begins with the location of status.py and then moves on to check the working
directory.
Args:
status_file: location of status.py, overridable for testing purposes.
Returns:
the path of mapreduce.yaml file or None if not found.
"""
checked = set()
yaml = _find_mapreduce_yaml(os.path.dirname(status_file), checked)
if not yaml:
yaml = _find_mapreduce_yaml(os.getcwd(), checked)
return yaml | python | def find_mapreduce_yaml(status_file=__file__):
checked = set()
yaml = _find_mapreduce_yaml(os.path.dirname(status_file), checked)
if not yaml:
yaml = _find_mapreduce_yaml(os.getcwd(), checked)
return yaml | [
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Begins with the location of status.py and then moves on to check the working
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Args:
status_file: location of status.py, overridable for testing purposes.
Returns:
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12,063 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | _find_mapreduce_yaml | def _find_mapreduce_yaml(start, checked):
"""Traverse the directory tree identified by start until a directory already
in checked is encountered or the path of mapreduce.yaml is found.
Checked is present both to make loop termination easy to reason about and so
that the same directories do not get rechecked.
Args:
start: the path to start in and work upward from
checked: the set of already examined directories
Returns:
the path of mapreduce.yaml file or None if not found.
"""
dir = start
while dir not in checked:
checked.add(dir)
for mr_yaml_name in MR_YAML_NAMES:
yaml_path = os.path.join(dir, mr_yaml_name)
if os.path.exists(yaml_path):
return yaml_path
dir = os.path.dirname(dir)
return None | python | def _find_mapreduce_yaml(start, checked):
dir = start
while dir not in checked:
checked.add(dir)
for mr_yaml_name in MR_YAML_NAMES:
yaml_path = os.path.join(dir, mr_yaml_name)
if os.path.exists(yaml_path):
return yaml_path
dir = os.path.dirname(dir)
return None | [
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12,064 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | parse_mapreduce_yaml | def parse_mapreduce_yaml(contents):
"""Parses mapreduce.yaml file contents.
Args:
contents: mapreduce.yaml file contents.
Returns:
MapReduceYaml object with all the data from original file.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file.
"""
try:
builder = yaml_object.ObjectBuilder(MapReduceYaml)
handler = yaml_builder.BuilderHandler(builder)
listener = yaml_listener.EventListener(handler)
listener.Parse(contents)
mr_info = handler.GetResults()
except (ValueError, yaml_errors.EventError), e:
raise errors.BadYamlError(e)
if len(mr_info) < 1:
raise errors.BadYamlError("No configs found in mapreduce.yaml")
if len(mr_info) > 1:
raise errors.MultipleDocumentsInMrYaml("Found %d YAML documents" %
len(mr_info))
jobs = mr_info[0]
job_names = set(j.name for j in jobs.mapreduce)
if len(jobs.mapreduce) != len(job_names):
raise errors.BadYamlError(
"Overlapping mapreduce names; names must be unique")
return jobs | python | def parse_mapreduce_yaml(contents):
try:
builder = yaml_object.ObjectBuilder(MapReduceYaml)
handler = yaml_builder.BuilderHandler(builder)
listener = yaml_listener.EventListener(handler)
listener.Parse(contents)
mr_info = handler.GetResults()
except (ValueError, yaml_errors.EventError), e:
raise errors.BadYamlError(e)
if len(mr_info) < 1:
raise errors.BadYamlError("No configs found in mapreduce.yaml")
if len(mr_info) > 1:
raise errors.MultipleDocumentsInMrYaml("Found %d YAML documents" %
len(mr_info))
jobs = mr_info[0]
job_names = set(j.name for j in jobs.mapreduce)
if len(jobs.mapreduce) != len(job_names):
raise errors.BadYamlError(
"Overlapping mapreduce names; names must be unique")
return jobs | [
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".... | Parses mapreduce.yaml file contents.
Args:
contents: mapreduce.yaml file contents.
Returns:
MapReduceYaml object with all the data from original file.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file. | [
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] | 2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6 | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/status.py#L205-L239 |
12,065 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | get_mapreduce_yaml | def get_mapreduce_yaml(parse=parse_mapreduce_yaml):
"""Locates mapreduce.yaml, loads and parses its info.
Args:
parse: Used for testing.
Returns:
MapReduceYaml object.
Raises:
errors.BadYamlError: when contents is not a valid mapreduce.yaml file or the
file is missing.
"""
mr_yaml_path = find_mapreduce_yaml()
if not mr_yaml_path:
raise errors.MissingYamlError()
mr_yaml_file = open(mr_yaml_path)
try:
return parse(mr_yaml_file.read())
finally:
mr_yaml_file.close() | python | def get_mapreduce_yaml(parse=parse_mapreduce_yaml):
mr_yaml_path = find_mapreduce_yaml()
if not mr_yaml_path:
raise errors.MissingYamlError()
mr_yaml_file = open(mr_yaml_path)
try:
return parse(mr_yaml_file.read())
finally:
mr_yaml_file.close() | [
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12,066 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/status.py | MapReduceYaml.to_dict | def to_dict(mapreduce_yaml):
"""Converts a MapReduceYaml file into a JSON-encodable dictionary.
For use in user-visible UI and internal methods for interfacing with
user code (like param validation). as a list
Args:
mapreduce_yaml: The Pyton representation of the mapreduce.yaml document.
Returns:
A list of configuration dictionaries.
"""
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs | python | def to_dict(mapreduce_yaml):
all_configs = []
for config in mapreduce_yaml.mapreduce:
out = {
"name": config.name,
"mapper_input_reader": config.mapper.input_reader,
"mapper_handler": config.mapper.handler,
}
if config.mapper.params_validator:
out["mapper_params_validator"] = config.mapper.params_validator
if config.mapper.params:
param_defaults = {}
for param in config.mapper.params:
param_defaults[param.name] = param.default or param.value
out["mapper_params"] = param_defaults
if config.params:
param_defaults = {}
for param in config.params:
param_defaults[param.name] = param.default or param.value
out["params"] = param_defaults
if config.mapper.output_writer:
out["mapper_output_writer"] = config.mapper.output_writer
all_configs.append(out)
return all_configs | [
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12,067 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _sort_records_map | def _sort_records_map(records):
"""Map function sorting records.
Converts records to KeyValue protos, sorts them by key and writes them
into new GCS file. Creates _OutputFile entity to record resulting
file name.
Args:
records: list of records which are serialized KeyValue protos.
"""
ctx = context.get()
l = len(records)
key_records = [None] * l
logging.debug("Parsing")
for i in range(l):
proto = kv_pb.KeyValue()
proto.ParseFromString(records[i])
key_records[i] = (proto.key(), records[i])
logging.debug("Sorting")
key_records.sort(cmp=_compare_keys)
logging.debug("Writing")
mapper_spec = ctx.mapreduce_spec.mapper
params = input_readers._get_params(mapper_spec)
bucket_name = params.get("bucket_name")
filename = (ctx.mapreduce_spec.name + "/" + ctx.mapreduce_id + "/output-" +
ctx.shard_id + "-" + str(int(time.time())))
full_filename = "/%s/%s" % (bucket_name, filename)
filehandle = cloudstorage.open(full_filename, mode="w")
with output_writers.GCSRecordsPool(filehandle, ctx=ctx) as pool:
for key_record in key_records:
pool.append(key_record[1])
logging.debug("Finalizing")
filehandle.close()
entity = _OutputFile(key_name=full_filename,
parent=_OutputFile.get_root_key(ctx.mapreduce_id))
entity.put() | python | def _sort_records_map(records):
ctx = context.get()
l = len(records)
key_records = [None] * l
logging.debug("Parsing")
for i in range(l):
proto = kv_pb.KeyValue()
proto.ParseFromString(records[i])
key_records[i] = (proto.key(), records[i])
logging.debug("Sorting")
key_records.sort(cmp=_compare_keys)
logging.debug("Writing")
mapper_spec = ctx.mapreduce_spec.mapper
params = input_readers._get_params(mapper_spec)
bucket_name = params.get("bucket_name")
filename = (ctx.mapreduce_spec.name + "/" + ctx.mapreduce_id + "/output-" +
ctx.shard_id + "-" + str(int(time.time())))
full_filename = "/%s/%s" % (bucket_name, filename)
filehandle = cloudstorage.open(full_filename, mode="w")
with output_writers.GCSRecordsPool(filehandle, ctx=ctx) as pool:
for key_record in key_records:
pool.append(key_record[1])
logging.debug("Finalizing")
filehandle.close()
entity = _OutputFile(key_name=full_filename,
parent=_OutputFile.get_root_key(ctx.mapreduce_id))
entity.put() | [
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12,068 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _merge_map | def _merge_map(key, values, partial):
"""A map function used in merge phase.
Stores (key, values) into KeyValues proto and yields its serialization.
Args:
key: values key.
values: values themselves.
partial: True if more values for this key will follow. False otherwise.
Yields:
The proto.
"""
proto = kv_pb.KeyValues()
proto.set_key(key)
proto.value_list().extend(values)
yield proto.Encode() | python | def _merge_map(key, values, partial):
proto = kv_pb.KeyValues()
proto.set_key(key)
proto.value_list().extend(values)
yield proto.Encode() | [
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12,069 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _hashing_map | def _hashing_map(binary_record):
"""A map function used in hash phase.
Reads KeyValue from binary record.
Args:
binary_record: The binary record.
Yields:
The (key, value).
"""
proto = kv_pb.KeyValue()
proto.ParseFromString(binary_record)
yield (proto.key(), proto.value()) | python | def _hashing_map(binary_record):
proto = kv_pb.KeyValue()
proto.ParseFromString(binary_record)
yield (proto.key(), proto.value()) | [
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12,070 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _MergingReader.split_input | def split_input(cls, mapper_spec):
"""Split input into multiple shards."""
filelists = mapper_spec.params[cls.FILES_PARAM]
max_values_count = mapper_spec.params.get(cls.MAX_VALUES_COUNT_PARAM, -1)
max_values_size = mapper_spec.params.get(cls.MAX_VALUES_SIZE_PARAM, -1)
return [cls([0] * len(files), max_values_count, max_values_size)
for files in filelists] | python | def split_input(cls, mapper_spec):
filelists = mapper_spec.params[cls.FILES_PARAM]
max_values_count = mapper_spec.params.get(cls.MAX_VALUES_COUNT_PARAM, -1)
max_values_size = mapper_spec.params.get(cls.MAX_VALUES_SIZE_PARAM, -1)
return [cls([0] * len(files), max_values_count, max_values_size)
for files in filelists] | [
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12,071 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _MergingReader.validate | def validate(cls, mapper_spec):
"""Validate reader parameters in mapper_spec."""
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = mapper_spec.params
if cls.FILES_PARAM not in params:
raise errors.BadReaderParamsError("Missing files parameter.") | python | def validate(cls, mapper_spec):
if mapper_spec.input_reader_class() != cls:
raise errors.BadReaderParamsError("Input reader class mismatch")
params = mapper_spec.params
if cls.FILES_PARAM not in params:
raise errors.BadReaderParamsError("Missing files parameter.") | [
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12,072 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.validate | def validate(cls, mapper_spec):
"""Validates mapper specification.
Args:
mapper_spec: an instance of model.MapperSpec to validate.
Raises:
BadWriterParamsError: when Output writer class mismatch.
"""
if mapper_spec.output_writer_class() != cls:
raise errors.BadWriterParamsError("Output writer class mismatch")
params = output_writers._get_params(mapper_spec)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in params:
raise errors.BadWriterParamsError(
"%s is required for the _HashingGCSOutputWriter" %
cls.BUCKET_NAME_PARAM) | python | def validate(cls, mapper_spec):
if mapper_spec.output_writer_class() != cls:
raise errors.BadWriterParamsError("Output writer class mismatch")
params = output_writers._get_params(mapper_spec)
# Bucket Name is required
if cls.BUCKET_NAME_PARAM not in params:
raise errors.BadWriterParamsError(
"%s is required for the _HashingGCSOutputWriter" %
cls.BUCKET_NAME_PARAM) | [
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12,073 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/shuffler.py | _HashingGCSOutputWriter.to_json | def to_json(self):
"""Returns writer state to serialize in json.
Returns:
A json-izable version of the OutputWriter state.
"""
# Use the member variable (since we don't have access to the context) to
# flush each pool to minimize the size of each filehandle before we
# serialize it.
for pool in self._pools:
if pool is not None:
pool.flush(True)
return {"filehandles": pickle.dumps(self._filehandles)} | python | def to_json(self):
# Use the member variable (since we don't have access to the context) to
# flush each pool to minimize the size of each filehandle before we
# serialize it.
for pool in self._pools:
if pool is not None:
pool.flush(True)
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12,074 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mapper_params | def _get_mapper_params(self):
"""Converts self to model.MapperSpec.params."""
reader_params = self.input_reader_cls.params_to_json(
self.input_reader_params)
# TODO(user): Do the same for writer params.
return {"input_reader": reader_params,
"output_writer": self.output_writer_params} | python | def _get_mapper_params(self):
reader_params = self.input_reader_cls.params_to_json(
self.input_reader_params)
# TODO(user): Do the same for writer params.
return {"input_reader": reader_params,
"output_writer": self.output_writer_params} | [
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12,075 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mapper_spec | def _get_mapper_spec(self):
"""Converts self to model.MapperSpec."""
# pylint: disable=g-import-not-at-top
from mapreduce import model
return model.MapperSpec(
handler_spec=util._obj_to_path(self.mapper),
input_reader_spec=util._obj_to_path(self.input_reader_cls),
params=self._get_mapper_params(),
shard_count=self.shard_count,
output_writer_spec=util._obj_to_path(self.output_writer_cls)) | python | def _get_mapper_spec(self):
# pylint: disable=g-import-not-at-top
from mapreduce import model
return model.MapperSpec(
handler_spec=util._obj_to_path(self.mapper),
input_reader_spec=util._obj_to_path(self.input_reader_cls),
params=self._get_mapper_params(),
shard_count=self.shard_count,
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12,076 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_mr_params | def _get_mr_params(self):
"""Converts self to model.MapreduceSpec.params."""
return {"force_writes": self._force_writes,
"done_callback": self.done_callback_url,
"user_params": self.user_params,
"shard_max_attempts": self.shard_max_attempts,
"task_max_attempts": self._task_max_attempts,
"task_max_data_processing_attempts":
self._task_max_data_processing_attempts,
"queue_name": self.queue_name,
"base_path": self._base_path,
"app_id": self._app,
"api_version": self._api_version} | python | def _get_mr_params(self):
return {"force_writes": self._force_writes,
"done_callback": self.done_callback_url,
"user_params": self.user_params,
"shard_max_attempts": self.shard_max_attempts,
"task_max_attempts": self._task_max_attempts,
"task_max_data_processing_attempts":
self._task_max_data_processing_attempts,
"queue_name": self.queue_name,
"base_path": self._base_path,
"app_id": self._app,
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12,077 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._get_default_mr_params | def _get_default_mr_params(cls):
"""Gets default values for old API."""
cfg = cls(_lenient=True)
mr_params = cfg._get_mr_params()
mr_params["api_version"] = 0
return mr_params | python | def _get_default_mr_params(cls):
cfg = cls(_lenient=True)
mr_params = cfg._get_mr_params()
mr_params["api_version"] = 0
return mr_params | [
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12,078 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_config.py | JobConfig._to_map_job_config | def _to_map_job_config(cls,
mr_spec,
# TODO(user): Remove this parameter after it can be
# read from mr_spec.
queue_name):
"""Converts model.MapreduceSpec back to JobConfig.
This method allows our internal methods to use JobConfig directly.
This method also allows us to expose JobConfig as an API during execution,
despite that it is not saved into datastore.
Args:
mr_spec: model.MapreduceSpec.
queue_name: queue name.
Returns:
The JobConfig object for this job.
"""
mapper_spec = mr_spec.mapper
# 0 means all the old APIs before api_version is introduced.
api_version = mr_spec.params.get("api_version", 0)
old_api = api_version == 0
# Deserialize params from json if input_reader/output_writer are new API.
input_reader_cls = mapper_spec.input_reader_class()
input_reader_params = input_readers._get_params(mapper_spec)
if issubclass(input_reader_cls, input_reader.InputReader):
input_reader_params = input_reader_cls.params_from_json(
input_reader_params)
output_writer_cls = mapper_spec.output_writer_class()
output_writer_params = output_writers._get_params(mapper_spec)
# TODO(user): Call json (de)serialization for writer.
# if (output_writer_cls and
# issubclass(output_writer_cls, output_writer.OutputWriter)):
# output_writer_params = output_writer_cls.params_from_json(
# output_writer_params)
# We can not always convert MapreduceSpec generated by older API
# to JobConfig. Thus, mr framework should use/expose the returned JobConfig
# object with caution when a job is started with an old API.
# In this case, this method only tries not to blow up and assemble a
# JobConfig object as accurate as possible.
return cls(_lenient=old_api,
job_name=mr_spec.name,
job_id=mr_spec.mapreduce_id,
# handler_spec from older API may not have map_job.Mapper type.
mapper=util.for_name(mapper_spec.handler_spec),
input_reader_cls=input_reader_cls,
input_reader_params=input_reader_params,
output_writer_cls=output_writer_cls,
output_writer_params=output_writer_params,
shard_count=mapper_spec.shard_count,
queue_name=queue_name,
user_params=mr_spec.params.get("user_params"),
shard_max_attempts=mr_spec.params.get("shard_max_attempts"),
done_callback_url=mr_spec.params.get("done_callback"),
_force_writes=mr_spec.params.get("force_writes"),
_base_path=mr_spec.params["base_path"],
_task_max_attempts=mr_spec.params.get("task_max_attempts"),
_task_max_data_processing_attempts=(
mr_spec.params.get("task_max_data_processing_attempts")),
_hooks_cls=util.for_name(mr_spec.hooks_class_name),
_app=mr_spec.params.get("app_id"),
_api_version=api_version) | python | def _to_map_job_config(cls,
mr_spec,
# TODO(user): Remove this parameter after it can be
# read from mr_spec.
queue_name):
mapper_spec = mr_spec.mapper
# 0 means all the old APIs before api_version is introduced.
api_version = mr_spec.params.get("api_version", 0)
old_api = api_version == 0
# Deserialize params from json if input_reader/output_writer are new API.
input_reader_cls = mapper_spec.input_reader_class()
input_reader_params = input_readers._get_params(mapper_spec)
if issubclass(input_reader_cls, input_reader.InputReader):
input_reader_params = input_reader_cls.params_from_json(
input_reader_params)
output_writer_cls = mapper_spec.output_writer_class()
output_writer_params = output_writers._get_params(mapper_spec)
# TODO(user): Call json (de)serialization for writer.
# if (output_writer_cls and
# issubclass(output_writer_cls, output_writer.OutputWriter)):
# output_writer_params = output_writer_cls.params_from_json(
# output_writer_params)
# We can not always convert MapreduceSpec generated by older API
# to JobConfig. Thus, mr framework should use/expose the returned JobConfig
# object with caution when a job is started with an old API.
# In this case, this method only tries not to blow up and assemble a
# JobConfig object as accurate as possible.
return cls(_lenient=old_api,
job_name=mr_spec.name,
job_id=mr_spec.mapreduce_id,
# handler_spec from older API may not have map_job.Mapper type.
mapper=util.for_name(mapper_spec.handler_spec),
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input_reader_params=input_reader_params,
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output_writer_params=output_writer_params,
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12,079 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter.__write_record | def __write_record(self, record_type, data):
"""Write single physical record."""
length = len(data)
crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
crc = crc32c.crc_update(crc, data)
crc = crc32c.crc_finalize(crc)
self.__writer.write(
struct.pack(_HEADER_FORMAT, _mask_crc(crc), length, record_type))
self.__writer.write(data)
self.__position += _HEADER_LENGTH + length | python | def __write_record(self, record_type, data):
length = len(data)
crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
crc = crc32c.crc_update(crc, data)
crc = crc32c.crc_finalize(crc)
self.__writer.write(
struct.pack(_HEADER_FORMAT, _mask_crc(crc), length, record_type))
self.__writer.write(data)
self.__position += _HEADER_LENGTH + length | [
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12,080 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter.write | def write(self, data):
"""Write single record.
Args:
data: record data to write as string, byte array or byte sequence.
"""
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
# Header won't fit into remainder
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self.__position += block_remaining
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self.__write_record(_RECORD_TYPE_FIRST, first_chunk)
data = data[len(first_chunk):]
while True:
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break
else:
chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_MIDDLE, chunk)
data = data[len(chunk):]
else:
self.__write_record(_RECORD_TYPE_FULL, data) | python | def write(self, data):
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
# Header won't fit into remainder
self.__writer.write('\x00' * block_remaining)
self.__position += block_remaining
block_remaining = _BLOCK_SIZE
if block_remaining < len(data) + _HEADER_LENGTH:
first_chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_FIRST, first_chunk)
data = data[len(first_chunk):]
while True:
block_remaining = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if block_remaining >= len(data) + _HEADER_LENGTH:
self.__write_record(_RECORD_TYPE_LAST, data)
break
else:
chunk = data[:block_remaining - _HEADER_LENGTH]
self.__write_record(_RECORD_TYPE_MIDDLE, chunk)
data = data[len(chunk):]
else:
self.__write_record(_RECORD_TYPE_FULL, data) | [
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12,081 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsWriter._pad_block | def _pad_block(self):
"""Pad block with 0.
Pad current block with 0. Reader will simply treat these as corrupted
record and skip the block.
This method is idempotent.
"""
pad_length = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
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self.__position += pad_length | python | def _pad_block(self):
pad_length = _BLOCK_SIZE - self.__position % _BLOCK_SIZE
if pad_length and pad_length != _BLOCK_SIZE:
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self.__position += pad_length | [
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12,082 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.__try_read_record | def __try_read_record(self):
"""Try reading a record.
Returns:
(data, record_type) tuple.
Raises:
EOFError: when end of file was reached.
InvalidRecordError: when valid record could not be read.
"""
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(masked_crc, length, record_type) = struct.unpack(_HEADER_FORMAT, header)
crc = _unmask_crc(masked_crc)
if length + _HEADER_LENGTH > block_remaining:
# A record can't be bigger than one block.
raise errors.InvalidRecordError('Length is too big')
data = self.__reader.read(length)
if len(data) != length:
raise EOFError('Not enough data read. Expected: %s but got %s' %
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if record_type == _RECORD_TYPE_NONE:
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actual_crc = crc32c.crc_update(actual_crc, data)
actual_crc = crc32c.crc_finalize(actual_crc)
if actual_crc != crc:
raise errors.InvalidRecordError('Data crc does not match')
return (data, record_type) | python | def __try_read_record(self):
block_remaining = _BLOCK_SIZE - self.__reader.tell() % _BLOCK_SIZE
if block_remaining < _HEADER_LENGTH:
return ('', _RECORD_TYPE_NONE)
header = self.__reader.read(_HEADER_LENGTH)
if len(header) != _HEADER_LENGTH:
raise EOFError('Read %s bytes instead of %s' %
(len(header), _HEADER_LENGTH))
(masked_crc, length, record_type) = struct.unpack(_HEADER_FORMAT, header)
crc = _unmask_crc(masked_crc)
if length + _HEADER_LENGTH > block_remaining:
# A record can't be bigger than one block.
raise errors.InvalidRecordError('Length is too big')
data = self.__reader.read(length)
if len(data) != length:
raise EOFError('Not enough data read. Expected: %s but got %s' %
(length, len(data)))
if record_type == _RECORD_TYPE_NONE:
return ('', record_type)
actual_crc = crc32c.crc_update(crc32c.CRC_INIT, [record_type])
actual_crc = crc32c.crc_update(actual_crc, data)
actual_crc = crc32c.crc_finalize(actual_crc)
if actual_crc != crc:
raise errors.InvalidRecordError('Data crc does not match')
return (data, record_type) | [
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12,083 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.__sync | def __sync(self):
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12,084 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/records.py | RecordsReader.read | def read(self):
"""Reads record from current position in reader.
Returns:
original bytes stored in a single record.
"""
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else:
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elif record_type == _RECORD_TYPE_LAST:
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else:
result = data + chunk
data = None
return result
else:
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except errors.InvalidRecordError, e:
logging.warning("Invalid record encountered at %s (%s). Syncing to "
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data = None
self.__sync() | python | def read(self):
data = None
while True:
last_offset = self.tell()
try:
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if record_type == _RECORD_TYPE_NONE:
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else:
data += chunk
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else:
result = data + chunk
data = None
return result
else:
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12,085 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/mapper_pipeline.py | MapperPipeline.run | def run(self,
job_name,
handler_spec,
input_reader_spec,
output_writer_spec=None,
params=None,
shards=None,
base_path=None):
"""Start a mapreduce job.
Args:
job_name: mapreduce name. Only for display purpose.
handler_spec: fully qualified name to your map function/class.
input_reader_spec: fully qualified name to input reader class.
output_writer_spec: fully qualified name to output writer class.
params: a dictionary of parameters for input reader and output writer
initialization.
shards: number of shards. This provides a guide to mapreduce. The real
number of shards is determined by how input are splited.
"""
if shards is None:
shards = parameters.config.SHARD_COUNT
if base_path is None:
base_path = parameters.config.BASE_PATH
mapreduce_id = control.start_map(
job_name,
handler_spec,
input_reader_spec,
params or {},
mapreduce_parameters={
"done_callback": self.get_callback_url(),
"done_callback_method": "GET",
"pipeline_id": self.pipeline_id,
"base_path": base_path,
},
shard_count=shards,
output_writer_spec=output_writer_spec,
queue_name=self.queue_name,
)
self.fill(self.outputs.job_id, mapreduce_id)
self.set_status(console_url="%s/detail?mapreduce_id=%s" % (
(base_path, mapreduce_id))) | python | def run(self,
job_name,
handler_spec,
input_reader_spec,
output_writer_spec=None,
params=None,
shards=None,
base_path=None):
if shards is None:
shards = parameters.config.SHARD_COUNT
if base_path is None:
base_path = parameters.config.BASE_PATH
mapreduce_id = control.start_map(
job_name,
handler_spec,
input_reader_spec,
params or {},
mapreduce_parameters={
"done_callback": self.get_callback_url(),
"done_callback_method": "GET",
"pipeline_id": self.pipeline_id,
"base_path": base_path,
},
shard_count=shards,
output_writer_spec=output_writer_spec,
queue_name=self.queue_name,
)
self.fill(self.outputs.job_id, mapreduce_id)
self.set_status(console_url="%s/detail?mapreduce_id=%s" % (
(base_path, mapreduce_id))) | [
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12,086 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/mapper_pipeline.py | MapperPipeline.callback | def callback(self):
"""Callback after this async pipeline finishes."""
if self.was_aborted:
return
mapreduce_id = self.outputs.job_id.value
mapreduce_state = model.MapreduceState.get_by_job_id(mapreduce_id)
if mapreduce_state.result_status != model.MapreduceState.RESULT_SUCCESS:
self.retry("Job %s had status %s" % (
mapreduce_id, mapreduce_state.result_status))
return
mapper_spec = mapreduce_state.mapreduce_spec.mapper
outputs = []
output_writer_class = mapper_spec.output_writer_class()
if (output_writer_class and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
outputs = output_writer_class.get_filenames(mapreduce_state)
self.fill(self.outputs.result_status, mapreduce_state.result_status)
self.fill(self.outputs.counters, mapreduce_state.counters_map.to_dict())
self.complete(outputs) | python | def callback(self):
if self.was_aborted:
return
mapreduce_id = self.outputs.job_id.value
mapreduce_state = model.MapreduceState.get_by_job_id(mapreduce_id)
if mapreduce_state.result_status != model.MapreduceState.RESULT_SUCCESS:
self.retry("Job %s had status %s" % (
mapreduce_id, mapreduce_state.result_status))
return
mapper_spec = mapreduce_state.mapreduce_spec.mapper
outputs = []
output_writer_class = mapper_spec.output_writer_class()
if (output_writer_class and
mapreduce_state.result_status == model.MapreduceState.RESULT_SUCCESS):
outputs = output_writer_class.get_filenames(mapreduce_state)
self.fill(self.outputs.result_status, mapreduce_state.result_status)
self.fill(self.outputs.counters, mapreduce_state.counters_map.to_dict())
self.complete(outputs) | [
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12,087 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/control.py | start_map | def start_map(name,
handler_spec,
reader_spec,
mapper_parameters,
shard_count=None,
output_writer_spec=None,
mapreduce_parameters=None,
base_path=None,
queue_name=None,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
"""Start a new, mapper-only mapreduce.
Deprecated! Use map_job.start instead.
If a value can be specified both from an explicit argument and from
a dictionary, the value from the explicit argument wins.
Args:
name: mapreduce name. Used only for display purposes.
handler_spec: fully qualified name of mapper handler function/class to call.
reader_spec: fully qualified name of mapper reader to use
mapper_parameters: dictionary of parameters to pass to mapper. These are
mapper-specific and also used for reader/writer initialization.
Should have format {"input_reader": {}, "output_writer":{}}. Old
deprecated style does not have sub dictionaries.
shard_count: number of shards to create.
mapreduce_parameters: dictionary of mapreduce parameters relevant to the
whole job.
base_path: base path of mapreduce library handler specified in app.yaml.
"/mapreduce" by default.
queue_name: taskqueue queue name to be used for mapreduce tasks.
see util.get_queue_name.
eta: absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: time in seconds into the future that this MR should execute.
Defaults to zero.
hooks_class_name: fully qualified name of a hooks.Hooks subclass.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
mapreduce id as string.
"""
if shard_count is None:
shard_count = parameters.config.SHARD_COUNT
if mapper_parameters:
mapper_parameters = dict(mapper_parameters)
# Make sure this old API fill all parameters with default values.
mr_params = map_job.JobConfig._get_default_mr_params()
if mapreduce_parameters:
mr_params.update(mapreduce_parameters)
# Override default values if user specified them as arguments.
if base_path:
mr_params["base_path"] = base_path
mr_params["queue_name"] = util.get_queue_name(queue_name)
mapper_spec = model.MapperSpec(handler_spec,
reader_spec,
mapper_parameters,
shard_count,
output_writer_spec=output_writer_spec)
if in_xg_transaction and not db.is_in_transaction():
logging.warning("Expects an opened xg transaction to start mapreduce "
"when transactional is True.")
return handlers.StartJobHandler._start_map(
name,
mapper_spec,
mr_params,
# TODO(user): Now that "queue_name" is part of mr_params.
# Remove all the other ways to get queue_name after one release.
queue_name=mr_params["queue_name"],
eta=eta,
countdown=countdown,
hooks_class_name=hooks_class_name,
_app=_app,
in_xg_transaction=in_xg_transaction) | python | def start_map(name,
handler_spec,
reader_spec,
mapper_parameters,
shard_count=None,
output_writer_spec=None,
mapreduce_parameters=None,
base_path=None,
queue_name=None,
eta=None,
countdown=None,
hooks_class_name=None,
_app=None,
in_xg_transaction=False):
if shard_count is None:
shard_count = parameters.config.SHARD_COUNT
if mapper_parameters:
mapper_parameters = dict(mapper_parameters)
# Make sure this old API fill all parameters with default values.
mr_params = map_job.JobConfig._get_default_mr_params()
if mapreduce_parameters:
mr_params.update(mapreduce_parameters)
# Override default values if user specified them as arguments.
if base_path:
mr_params["base_path"] = base_path
mr_params["queue_name"] = util.get_queue_name(queue_name)
mapper_spec = model.MapperSpec(handler_spec,
reader_spec,
mapper_parameters,
shard_count,
output_writer_spec=output_writer_spec)
if in_xg_transaction and not db.is_in_transaction():
logging.warning("Expects an opened xg transaction to start mapreduce "
"when transactional is True.")
return handlers.StartJobHandler._start_map(
name,
mapper_spec,
mr_params,
# TODO(user): Now that "queue_name" is part of mr_params.
# Remove all the other ways to get queue_name after one release.
queue_name=mr_params["queue_name"],
eta=eta,
countdown=countdown,
hooks_class_name=hooks_class_name,
_app=_app,
in_xg_transaction=in_xg_transaction) | [
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Args:
name: mapreduce name. Used only for display purposes.
handler_spec: fully qualified name of mapper handler function/class to call.
reader_spec: fully qualified name of mapper reader to use
mapper_parameters: dictionary of parameters to pass to mapper. These are
mapper-specific and also used for reader/writer initialization.
Should have format {"input_reader": {}, "output_writer":{}}. Old
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shard_count: number of shards to create.
mapreduce_parameters: dictionary of mapreduce parameters relevant to the
whole job.
base_path: base path of mapreduce library handler specified in app.yaml.
"/mapreduce" by default.
queue_name: taskqueue queue name to be used for mapreduce tasks.
see util.get_queue_name.
eta: absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
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countdown: time in seconds into the future that this MR should execute.
Defaults to zero.
hooks_class_name: fully qualified name of a hooks.Hooks subclass.
in_xg_transaction: controls what transaction scope to use to start this MR
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mapreduce id as string. | [
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12,088 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_status | def get_status(self):
"""Get status enum.
Returns:
One of the status enum.
"""
self.__update_state()
if self._state.active:
return self.RUNNING
else:
return self._state.result_status | python | def get_status(self):
self.__update_state()
if self._state.active:
return self.RUNNING
else:
return self._state.result_status | [
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12,089 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_counter | def get_counter(self, counter_name, default=0):
"""Get the value of the named counter from this job.
When a job is running, counter values won't be very accurate.
Args:
counter_name: name of the counter in string.
default: default value if the counter doesn't exist.
Returns:
Value in int of the named counter.
"""
self.__update_state()
return self._state.counters_map.get(counter_name, default) | python | def get_counter(self, counter_name, default=0):
self.__update_state()
return self._state.counters_map.get(counter_name, default) | [
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12,090 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.get_outputs | def get_outputs(self):
"""Get outputs of this job.
Should only call if status is SUCCESS.
Yields:
Iterators, one for each shard. Each iterator is
from the argument of map_job.output_writer.commit_output.
"""
assert self.SUCCESS == self.get_status()
ss = model.ShardState.find_all_by_mapreduce_state(self._state)
for s in ss:
yield iter(s.writer_state.get("outs", [])) | python | def get_outputs(self):
assert self.SUCCESS == self.get_status()
ss = model.ShardState.find_all_by_mapreduce_state(self._state)
for s in ss:
yield iter(s.writer_state.get("outs", [])) | [
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12,091 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.submit | def submit(cls, job_config, in_xg_transaction=False):
"""Submit the job to run.
Args:
job_config: an instance of map_job.MapJobConfig.
in_xg_transaction: controls what transaction scope to use to start this MR
job. If True, there has to be an already opened cross-group transaction
scope. MR will use one entity group from it.
If False, MR will create an independent transaction to start the job
regardless of any existing transaction scopes.
Returns:
a Job instance representing the submitted job.
"""
cls.__validate_job_config(job_config)
mapper_spec = job_config._get_mapper_spec()
# Create mr spec.
mapreduce_params = job_config._get_mr_params()
mapreduce_spec = model.MapreduceSpec(
job_config.job_name,
job_config.job_id,
mapper_spec.to_json(),
mapreduce_params,
util._obj_to_path(job_config._hooks_cls))
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
state = None
@db.transactional(propagation=propagation)
def _txn():
state = cls.__create_and_save_state(job_config, mapreduce_spec)
cls.__add_kickoff_task(job_config, mapreduce_spec)
return state
state = _txn()
return cls(state) | python | def submit(cls, job_config, in_xg_transaction=False):
cls.__validate_job_config(job_config)
mapper_spec = job_config._get_mapper_spec()
# Create mr spec.
mapreduce_params = job_config._get_mr_params()
mapreduce_spec = model.MapreduceSpec(
job_config.job_name,
job_config.job_id,
mapper_spec.to_json(),
mapreduce_params,
util._obj_to_path(job_config._hooks_cls))
# Save states and enqueue task.
if in_xg_transaction:
propagation = db.MANDATORY
else:
propagation = db.INDEPENDENT
state = None
@db.transactional(propagation=propagation)
def _txn():
state = cls.__create_and_save_state(job_config, mapreduce_spec)
cls.__add_kickoff_task(job_config, mapreduce_spec)
return state
state = _txn()
return cls(state) | [
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12,092 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__update_state | def __update_state(self):
"""Fetches most up to date state from db."""
# Only if the job was not in a terminal state.
if self._state.active:
self._state = self.__get_state_by_id(self.job_config.job_id) | python | def __update_state(self):
# Only if the job was not in a terminal state.
if self._state.active:
self._state = self.__get_state_by_id(self.job_config.job_id) | [
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12,093 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__get_state_by_id | def __get_state_by_id(cls, job_id):
"""Get job state by id.
Args:
job_id: job id.
Returns:
model.MapreduceState for the job.
Raises:
ValueError: if the job state is missing.
"""
state = model.MapreduceState.get_by_job_id(job_id)
if state is None:
raise ValueError("Job state for job %s is missing." % job_id)
return state | python | def __get_state_by_id(cls, job_id):
state = model.MapreduceState.get_by_job_id(job_id)
if state is None:
raise ValueError("Job state for job %s is missing." % job_id)
return state | [
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12,094 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__create_and_save_state | def __create_and_save_state(cls, job_config, mapreduce_spec):
"""Save map job state to datastore.
Save state to datastore so that UI can see it immediately.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec.
Returns:
model.MapreduceState for this job.
"""
state = model.MapreduceState.create_new(job_config.job_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
state.app_id = job_config._app
config = datastore_rpc.Configuration(force_writes=job_config._force_writes)
state.put(config=config)
return state | python | def __create_and_save_state(cls, job_config, mapreduce_spec):
state = model.MapreduceState.create_new(job_config.job_id)
state.mapreduce_spec = mapreduce_spec
state.active = True
state.active_shards = 0
state.app_id = job_config._app
config = datastore_rpc.Configuration(force_writes=job_config._force_writes)
state.put(config=config)
return state | [
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Save state to datastore so that UI can see it immediately.
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job_config: map_job.JobConfig.
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12,095 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/api/map_job/map_job_control.py | Job.__add_kickoff_task | def __add_kickoff_task(cls, job_config, mapreduce_spec):
"""Add kickoff task to taskqueue.
Args:
job_config: map_job.JobConfig.
mapreduce_spec: model.MapreduceSpec,
"""
params = {"mapreduce_id": job_config.job_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
# TODO(user): Perhaps make this url a computed field of job_config.
url=job_config._base_path + "/kickoffjob_callback/" + job_config.job_id,
headers=util._get_task_headers(job_config.job_id),
params=params)
if job_config._hooks_cls:
hooks = job_config._hooks_cls(mapreduce_spec)
try:
hooks.enqueue_kickoff_task(kickoff_task, job_config.queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(job_config.queue_name, transactional=True) | python | def __add_kickoff_task(cls, job_config, mapreduce_spec):
params = {"mapreduce_id": job_config.job_id}
# Task is not named so that it can be added within a transaction.
kickoff_task = taskqueue.Task(
# TODO(user): Perhaps make this url a computed field of job_config.
url=job_config._base_path + "/kickoffjob_callback/" + job_config.job_id,
headers=util._get_task_headers(job_config.job_id),
params=params)
if job_config._hooks_cls:
hooks = job_config._hooks_cls(mapreduce_spec)
try:
hooks.enqueue_kickoff_task(kickoff_task, job_config.queue_name)
return
except NotImplementedError:
pass
kickoff_task.add(job_config.queue_name, transactional=True) | [
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12,096 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonMixin.to_json_str | def to_json_str(self):
"""Convert data to json string representation.
Returns:
json representation as string.
"""
_json = self.to_json()
try:
return json.dumps(_json, sort_keys=True, cls=JsonEncoder)
except:
logging.exception("Could not serialize JSON: %r", _json)
raise | python | def to_json_str(self):
_json = self.to_json()
try:
return json.dumps(_json, sort_keys=True, cls=JsonEncoder)
except:
logging.exception("Could not serialize JSON: %r", _json)
raise | [
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Returns:
json representation as string. | [
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12,097 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonMixin.from_json_str | def from_json_str(cls, json_str):
"""Convert json string representation into class instance.
Args:
json_str: json representation as string.
Returns:
New instance of the class with data loaded from json string.
"""
return cls.from_json(json.loads(json_str, cls=JsonDecoder)) | python | def from_json_str(cls, json_str):
return cls.from_json(json.loads(json_str, cls=JsonDecoder)) | [
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Args:
json_str: json representation as string.
Returns:
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12,098 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonProperty.get_value_for_datastore | def get_value_for_datastore(self, model_instance):
"""Gets value for datastore.
Args:
model_instance: instance of the model class.
Returns:
datastore-compatible value.
"""
value = super(JsonProperty, self).get_value_for_datastore(model_instance)
if not value:
return None
json_value = value
if not isinstance(value, dict):
json_value = value.to_json()
if not json_value:
return None
return datastore_types.Text(json.dumps(
json_value, sort_keys=True, cls=JsonEncoder)) | python | def get_value_for_datastore(self, model_instance):
value = super(JsonProperty, self).get_value_for_datastore(model_instance)
if not value:
return None
json_value = value
if not isinstance(value, dict):
json_value = value.to_json()
if not json_value:
return None
return datastore_types.Text(json.dumps(
json_value, sort_keys=True, cls=JsonEncoder)) | [
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Args:
model_instance: instance of the model class.
Returns:
datastore-compatible value. | [
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] | 2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6 | https://github.com/GoogleCloudPlatform/appengine-mapreduce/blob/2045eb3605b6ecb40c83d11dd5442a89fe5c5dd6/python/src/mapreduce/json_util.py#L183-L201 |
12,099 | GoogleCloudPlatform/appengine-mapreduce | python/src/mapreduce/json_util.py | JsonProperty.make_value_from_datastore | def make_value_from_datastore(self, value):
"""Convert value from datastore representation.
Args:
value: datastore value.
Returns:
value to store in the model.
"""
if value is None:
return None
_json = json.loads(value, cls=JsonDecoder)
if self.data_type == dict:
return _json
return self.data_type.from_json(_json) | python | def make_value_from_datastore(self, value):
if value is None:
return None
_json = json.loads(value, cls=JsonDecoder)
if self.data_type == dict:
return _json
return self.data_type.from_json(_json) | [
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Args:
value: datastore value.
Returns:
value to store in the model. | [
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