def get_response_content(fs):
# get the tree
tree = NewickIO.parse(fs.tree, FelTree.NewickTree)
ordered_names = list(sorted(node.name for node in tree.gen_tips()))
n = len(ordered_names)
if n < 2:
raise HandlingError('the newick tree should have at least two leaves')
# get the eigendecomposition
D = np.array(tree.get_distance_matrix(ordered_names))
G = (-0.5) * MatrixUtil.double_centered(D)
eigenvalues, eigenvector_transposes = np.linalg.eigh(G)
eigenvectors = eigenvector_transposes.T
sorted_eigensystem = list(reversed(list(sorted((w, v) for w, v in zip(eigenvalues, eigenvectors)))))
sorted_eigenvalues, sorted_eigenvectors = zip(*sorted_eigensystem)
M = zip(*sorted_eigenvectors)
# write the html
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
print >> out, HtmlTable.get_labeled_table_string(
sorted_eigenvalues, ordered_names, M)
print >> out, '</body>'
print >> out, '</html>'
# write the response
return out.getvalue()
def get_response_content(fs):
# get the tree
tree = NewickIO.parse(fs.tree, FelTree.NewickTree)
# define the ordering of the nodes
ordered_names = list(sorted(node.get_name() for node in tree.preorder()))
name_to_id = dict((node.get_name(), id(node)) for node in tree.preorder())
ordered_ids = [name_to_id[name] for name in ordered_names]
# get the affinity matrix
A = np.array(tree.get_affinity_matrix(ordered_ids))
# get the normalized laplacian
for row, name in zip(A, ordered_names):
assert sum(row), name
row_sums = [sum(row) for row in A]
n = len(A)
L_script = np.zeros((n, n))
for i in range(n):
for j in range(n):
if i == j:
L_script[i][j] = 1
L_script[i][j] -= A[i][j] / math.sqrt(row_sums[i] * row_sums[j])
# get the eigendecomposition
eigenvalues, eigenvector_transposes = np.linalg.eigh(L_script)
eigenvectors = eigenvector_transposes.T
eigensystem = [(abs(w), w, v.tolist())
for w, v in zip(eigenvalues, eigenvectors)]
sorted_eigensystem = list(reversed(sorted(eigensystem)))
sorted_abs_eigenvalues, sorted_eigenvalues, sorted_eigenvectors = zip(
*sorted_eigensystem)
M = zip(*sorted_eigenvectors)
# turn the numbers into strings
format_string = '%%.%df' % fs.precision
M_strings = [[format_string % value for value in row] for row in M]
sorted_eigenvalue_strings = [format_string % w for w in sorted_eigenvalues]
# write the html
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
print >> out, HtmlTable.get_labeled_table_string(sorted_eigenvalue_strings,
ordered_names, M_strings)
print >> out, '</body>'
print >> out, '</html>'
# write the response
return out.getvalue()
def cpuMem(namespace, caption):
titles = ["POD", "Container", "Resources"]
formats = ["str", "str", "str"]
listCpuMem = []
pods = helper.getPodStatus(namespace)
for pod in pods:
if (pod.startswith("NAME READY STATUS RESTARTS AGE")):
continue
values = pod.split(" ")
if (len(values) < 5):
continue
podname = values[0]
#for prefix in config.grafana_pods:
# if(podname.startswith(prefix)):
cpuMemInfo = cpumem.containerCpuMem(namespace, podname)
listCpuMem += cpuMemInfo
result = htmlTable.table(titles, formats, listCpuMem, caption)
return result, pods
def get_response_content(fs):
# get the tree
tree = NewickIO.parse(fs.tree, FelTree.NewickTree)
# define the ordering of the nodes
ordered_names = list(sorted(node.get_name() for node in tree.preorder()))
name_to_id = dict((node.get_name(), id(node)) for node in tree.preorder())
ordered_ids = [name_to_id[name] for name in ordered_names]
# get the affinity matrix
A = np.array(tree.get_affinity_matrix(ordered_ids))
# get the normalized laplacian
for row, name in zip(A, ordered_names):
assert sum(row), name
row_sums = [sum(row) for row in A]
n = len(A)
L_script = np.zeros((n,n))
for i in range(n):
for j in range(n):
if i == j:
L_script[i][j] = 1
L_script[i][j] -= A[i][j] / math.sqrt(row_sums[i]*row_sums[j])
# get the eigendecomposition
eigenvalues, eigenvector_transposes = np.linalg.eigh(L_script)
eigenvectors = eigenvector_transposes.T
eigensystem = [(abs(w), w, v.tolist()) for w, v in zip(eigenvalues, eigenvectors)]
sorted_eigensystem = list(reversed(sorted(eigensystem)))
sorted_abs_eigenvalues, sorted_eigenvalues, sorted_eigenvectors = zip(*sorted_eigensystem)
M = zip(*sorted_eigenvectors)
# turn the numbers into strings
format_string = '%%.%df' % fs.precision
M_strings = [[format_string % value for value in row] for row in M]
sorted_eigenvalue_strings = [format_string % w for w in sorted_eigenvalues]
# write the html
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
print >> out, HtmlTable.get_labeled_table_string(
sorted_eigenvalue_strings, ordered_names, M_strings)
print >> out, '</body>'
print >> out, '</html>'
# write the response
return out.getvalue()
def get_response_content(fs):
# get the tree
tree = NewickIO.parse(fs.tree, FelTree.NewickTree)
ordered_names = list(sorted(node.name for node in tree.gen_tips()))
n = len(ordered_names)
if n < 2:
raise HandlingError("the newick tree should have at least two leaves")
# get the eigendecomposition
D = np.array(tree.get_distance_matrix(ordered_names))
G = (-0.5) * MatrixUtil.double_centered(D)
eigenvalues, eigenvector_transposes = np.linalg.eigh(G)
eigenvectors = eigenvector_transposes.T
sorted_eigensystem = list(reversed(list(sorted((w, v) for w, v in zip(eigenvalues, eigenvectors)))))
sorted_eigenvalues, sorted_eigenvectors = zip(*sorted_eigensystem)
M = zip(*sorted_eigenvectors)
# write the html
out = StringIO()
print >> out, "<html>"
print >> out, "<body>"
print >> out, HtmlTable.get_labeled_table_string(sorted_eigenvalues, ordered_names, M)
print >> out, "</body>"
print >> out, "</html>"
# write the response
return out.getvalue()
def do_command_line_analysis(options):
"""
Print some stuff to stdout, and show a progress bar on stderr.
@param options: an object from optparse
"""
# load the tree, using the default tree if no filename was provided
tree, tree_remark = get_tree_and_remark(options)
# initialize the simulation objects
sims = [
Simulation(Clustering.NeighborJoiningDMS(), 'nj', 'neighbor joining'),
Simulation(Clustering.StoneSpectralSignDMS(), 'nj',
'spectral sign cut with neighbor joining fallback'),
Simulation(Clustering.RandomDMS(), 'nj', 'random partitioning')
]
# possibly add the slow simulation
if options.use_exact:
sims.append(
Simulation(Clustering.StoneExactDMS(), 'nj',
'exact criterion with neighbor joining fallback'))
# define the simulation parameters
reconstruction_count = options.nsamples
sequence_length_string = options.sequence_length
if sequence_length_string == 'inf':
sequence_length = float('inf')
else:
sequence_length = int(sequence_length_string)
inf_replacement = 20.0
if options.reject_inf:
inf_replacement = None
elif options.replace_inf:
try:
inf_replacement = float(options.replace_inf)
except ValueError:
msg = 'invalid replace_inf value: '
raise OptionError(msg + str(options.replace_inf))
zero_replacement = 0
if options.reject_zero:
zero_replacement = None
elif options.replace_zero:
try:
zero_replacement = float(options.replace_zero)
except ValueError:
msg = 'invalid replace_zero value: '
raise OptionError(msg + str(options.replace_zero))
# start the html file
print '<html><body>'
# show the simulation parameters
print 'original tree source:', tree_remark, '<br/>'
print 'reconstruction count:', reconstruction_count, '<br/>'
print 'sequence length:', sequence_length, '<br/>'
# set the simulation parameters for each simulation
for sim in sims:
sim.set_original_tree(tree)
# If there is only one reconstruction per method
# then show the progress of the tree builder.
if reconstruction_count == 1:
sim.set_verbose()
# define an arbitrary but consistent ordering of the taxa
ordered_names = [node.name for node in tree.gen_tips()]
try:
# attempt to simulate a bunch of distance matrices
if options.verbose:
print 'sampling', reconstruction_count, 'distance matrices...'
# initialize the distance matrix sampler
sampler = DMSampler.DMSampler(tree, ordered_names, sequence_length)
sampler.set_inf_replacement(inf_replacement)
sampler.set_zero_replacement(zero_replacement)
# start the progress bar
pbar = Progress.Bar(1.0)
# sample some distance matrices
distance_matrices = []
for result in sampler.gen_samples_or_none():
# if we got a result then update the distance matrix list
if result:
sequence_list, D = result
distance_matrices.append(D)
# Update the progressbar regardless of whether or not
# the proposal was accepted.
remaining_acceptances = reconstruction_count - len(
distance_matrices)
numerator = sampler.get_completed_proposals()
denominator = numerator + sampler.get_remaining_proposals(
remaining_acceptances)
dms_fraction = float(numerator) / float(denominator)
dms_total = 1.0 / (1 + len(sims))
pbar.update(dms_fraction * dms_total)
# if we have enough samples then break the loop
if not remaining_acceptances:
break
# reconstruct trees using various methods
for i, sim in enumerate(sims):
if options.verbose:
print 'running "%s"...' % sim.description
sim.run(distance_matrices, ordered_names)
pbar.update(float(i + 2) / float(1 + len(sims)))
# stop the progress bar
pbar.finish()
# get the simulation data
table = [('method', 'seconds', 'uniform loss', 'weighted loss')]
for sim in sims:
table.append((sim.description, sim.get_running_time(),
sim.get_uniform_loss(), sim.get_deep_loss()))
# convert the row major matrix into an html table
print HtmlTable.get_table_string(table)
# end the html file
print '</html></body>'
except KeyboardInterrupt:
print 'interrupted stage', pbar.progress, 'of', pbar.high
def get_response_content(fs):
# start writing the html response
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
# get the tree and the column sent by the user
pruned_tree, taxon_to_aa_letter = get_tree_and_column(fs)
# get the weights of the taxa
taxon_weight_pairs = LeafWeights.get_stone_weights(pruned_tree)
# show the raw physicochemical property table
if fs.show_raw_pc_table:
print >> out, 'raw physicochemical property table:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = MAPP.g_property_array
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the standardized physicochemical property table
standardized_property_array = MAPP.get_standardized_property_array(
MAPP.g_property_array)
# show the standardized physicochemical property table
if fs.show_standardized_pc_table:
print >> out, 'standardized physicochemical property table:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = standardized_property_array
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the physicochemical property correlation matrix
correlation_matrix = MAPP.get_property_correlation_matrix(
standardized_property_array)
# show the physicochemical property correlation matrix
if fs.show_pc_correlation_matrix:
print >> out, 'physicochemical property correlation matrix:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = MAPP.g_property_names
table = correlation_matrix
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# show the pruned tree
if fs.show_tree:
tree_string = NewickIO.get_narrow_newick_string(pruned_tree, 80)
lines = StringIO(tree_string).readlines()
lines = [line.rstrip() for line in lines]
print >> out, 'pruned phylogenetic tree in newick format:'
print >> out, '<pre>'
for line in lines:
print >> out, cgi.escape(line)
print >> out, '</pre>'
print >> out, '<br/>'
# show the weights
if fs.show_weights:
taxa, weights = zip(*taxon_weight_pairs)
table = [weights]
row_labels = ['weight']
col_labels = taxa
print >> out, 'taxon weights:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# estimate the amino acid distribution for the column,
# taking into account the tree and a uniform prior.
weights = []
aa_indices = []
for taxon, weight in taxon_weight_pairs:
weights.append(weight)
aa_indices.append(aa_letter_to_aa_index(taxon_to_aa_letter[taxon]))
aa_distribution = MAPP.estimate_aa_distribution(weights, aa_indices)
# show the estimated amino acid distribution
if fs.show_aa_distribution:
table = [aa_distribution]
row_labels = ['weight']
col_labels = Codon.g_aa_letters
print >> out, 'estimated amino acid distribution:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# estimate the mean and variance of each physicochemical property
est_pc_means = MAPP.estimate_property_means(
standardized_property_array, aa_distribution)
est_pc_variances = MAPP.estimate_property_variances(
standardized_property_array, aa_distribution)
# show the estimated mean and variance of each physicochemical property
if fs.show_pc_distribution:
table = [est_pc_means, est_pc_variances]
row_labels = ['mean', 'variance']
col_labels = MAPP.g_property_names
print >> out, 'estimated physicochemical property moments:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the deviation from each property mean
# for each possible amino acid
deviations = MAPP.get_deviations(
est_pc_means, est_pc_variances, standardized_property_array)
# show the deviation from each property mean for each possible amino acid
if fs.show_deviations:
print >> out, 'deviations of amino acids from the normal distribution'
print >> out, 'estimated for each property:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = deviations
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the impact scores
impact_scores = MAPP.get_impact_scores(correlation_matrix, deviations)
# show the impact scores
if fs.show_impact_scores:
table = [impact_scores]
row_labels = ['impact']
col_labels = Codon.g_aa_letters
print >> out, 'impact scores:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the p-values
p_values = []
for score in impact_scores:
ntaxa = len(taxon_weight_pairs)
p_values.append(MAPP.get_p_value(score, ntaxa))
# show the p-values
if fs.show_p_values:
table = [p_values]
row_labels = ['p-value']
col_labels = Codon.g_aa_letters
print >> out, 'p-values:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# write the html footer
print >> out, '</body>'
print >> out, '</html>'
# return the response
return out.getvalue()
def do_command_line_analysis(options):
"""
Print some stuff to stdout, and show a progress bar on stderr.
@param options: an object from optparse
"""
# load the tree, using the default tree if no filename was provided
tree, tree_remark = get_tree_and_remark(options)
# initialize the simulation objects
sims = [
Simulation(Clustering.NeighborJoiningDMS(), "nj", "neighbor joining"),
Simulation(Clustering.StoneSpectralSignDMS(), "nj", "spectral sign cut with neighbor joining fallback"),
Simulation(Clustering.RandomDMS(), "nj", "random partitioning"),
]
# possibly add the slow simulation
if options.use_exact:
sims.append(Simulation(Clustering.StoneExactDMS(), "nj", "exact criterion with neighbor joining fallback"))
# define the simulation parameters
reconstruction_count = options.nsamples
sequence_length_string = options.sequence_length
if sequence_length_string == "inf":
sequence_length = float("inf")
else:
sequence_length = int(sequence_length_string)
inf_replacement = 20.0
if options.reject_inf:
inf_replacement = None
elif options.replace_inf:
try:
inf_replacement = float(options.replace_inf)
except ValueError:
msg = "invalid replace_inf value: "
raise OptionError(msg + str(options.replace_inf))
zero_replacement = 0
if options.reject_zero:
zero_replacement = None
elif options.replace_zero:
try:
zero_replacement = float(options.replace_zero)
except ValueError:
msg = "invalid replace_zero value: "
raise OptionError(msg + str(options.replace_zero))
# start the html file
print "<html><body>"
# show the simulation parameters
print "original tree source:", tree_remark, "<br/>"
print "reconstruction count:", reconstruction_count, "<br/>"
print "sequence length:", sequence_length, "<br/>"
# set the simulation parameters for each simulation
for sim in sims:
sim.set_original_tree(tree)
# If there is only one reconstruction per method
# then show the progress of the tree builder.
if reconstruction_count == 1:
sim.set_verbose()
# define an arbitrary but consistent ordering of the taxa
ordered_names = [node.name for node in tree.gen_tips()]
try:
# attempt to simulate a bunch of distance matrices
if options.verbose:
print "sampling", reconstruction_count, "distance matrices..."
# initialize the distance matrix sampler
sampler = DMSampler.DMSampler(tree, ordered_names, sequence_length)
sampler.set_inf_replacement(inf_replacement)
sampler.set_zero_replacement(zero_replacement)
# start the progress bar
pbar = Progress.Bar(1.0)
# sample some distance matrices
distance_matrices = []
for result in sampler.gen_samples_or_none():
# if we got a result then update the distance matrix list
if result:
sequence_list, D = result
distance_matrices.append(D)
# Update the progressbar regardless of whether or not
# the proposal was accepted.
remaining_acceptances = reconstruction_count - len(distance_matrices)
numerator = sampler.get_completed_proposals()
denominator = numerator + sampler.get_remaining_proposals(remaining_acceptances)
dms_fraction = float(numerator) / float(denominator)
dms_total = 1.0 / (1 + len(sims))
pbar.update(dms_fraction * dms_total)
# if we have enough samples then break the loop
if not remaining_acceptances:
break
# reconstruct trees using various methods
for i, sim in enumerate(sims):
if options.verbose:
print 'running "%s"...' % sim.description
sim.run(distance_matrices, ordered_names)
pbar.update(float(i + 2) / float(1 + len(sims)))
# stop the progress bar
pbar.finish()
# get the simulation data
table = [("method", "seconds", "uniform loss", "weighted loss")]
for sim in sims:
table.append((sim.description, sim.get_running_time(), sim.get_uniform_loss(), sim.get_deep_loss()))
# convert the row major matrix into an html table
print HtmlTable.get_table_string(table)
# end the html file
print "</html></body>"
except KeyboardInterrupt:
print "interrupted stage", pbar.progress, "of", pbar.high
def get_response_content(fs):
# start writing the html response
out = StringIO()
print >> out, '<html>'
print >> out, '<body>'
# get the tree and the column sent by the user
pruned_tree, taxon_to_aa_letter = get_tree_and_column(fs)
# get the weights of the taxa
taxon_weight_pairs = LeafWeights.get_stone_weights(pruned_tree)
# show the raw physicochemical property table
if fs.show_raw_pc_table:
print >> out, 'raw physicochemical property table:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = MAPP.g_property_array
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the standardized physicochemical property table
standardized_property_array = MAPP.get_standardized_property_array(
MAPP.g_property_array)
# show the standardized physicochemical property table
if fs.show_standardized_pc_table:
print >> out, 'standardized physicochemical property table:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = standardized_property_array
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the physicochemical property correlation matrix
correlation_matrix = MAPP.get_property_correlation_matrix(
standardized_property_array)
# show the physicochemical property correlation matrix
if fs.show_pc_correlation_matrix:
print >> out, 'physicochemical property correlation matrix:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = MAPP.g_property_names
table = correlation_matrix
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# show the pruned tree
if fs.show_tree:
tree_string = NewickIO.get_narrow_newick_string(pruned_tree, 80)
lines = StringIO(tree_string).readlines()
lines = [line.rstrip() for line in lines]
print >> out, 'pruned phylogenetic tree in newick format:'
print >> out, '<pre>'
for line in lines:
print >> out, cgi.escape(line)
print >> out, '</pre>'
print >> out, '<br/>'
# show the weights
if fs.show_weights:
taxa, weights = zip(*taxon_weight_pairs)
table = [weights]
row_labels = ['weight']
col_labels = taxa
print >> out, 'taxon weights:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# estimate the amino acid distribution for the column,
# taking into account the tree and a uniform prior.
weights = []
aa_indices = []
for taxon, weight in taxon_weight_pairs:
weights.append(weight)
aa_indices.append(aa_letter_to_aa_index(taxon_to_aa_letter[taxon]))
aa_distribution = MAPP.estimate_aa_distribution(weights, aa_indices)
# show the estimated amino acid distribution
if fs.show_aa_distribution:
table = [aa_distribution]
row_labels = ['weight']
col_labels = Codon.g_aa_letters
print >> out, 'estimated amino acid distribution:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# estimate the mean and variance of each physicochemical property
est_pc_means = MAPP.estimate_property_means(standardized_property_array,
aa_distribution)
est_pc_variances = MAPP.estimate_property_variances(
standardized_property_array, aa_distribution)
# show the estimated mean and variance of each physicochemical property
if fs.show_pc_distribution:
table = [est_pc_means, est_pc_variances]
row_labels = ['mean', 'variance']
col_labels = MAPP.g_property_names
print >> out, 'estimated physicochemical property moments:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the deviation from each property mean
# for each possible amino acid
deviations = MAPP.get_deviations(est_pc_means, est_pc_variances,
standardized_property_array)
# show the deviation from each property mean for each possible amino acid
if fs.show_deviations:
print >> out, 'deviations of amino acids from the normal distribution'
print >> out, 'estimated for each property:'
print >> out, '<br/>'
col_labels = MAPP.g_property_names
row_labels = Codon.g_aa_letters
table = deviations
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the impact scores
impact_scores = MAPP.get_impact_scores(correlation_matrix, deviations)
# show the impact scores
if fs.show_impact_scores:
table = [impact_scores]
row_labels = ['impact']
col_labels = Codon.g_aa_letters
print >> out, 'impact scores:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# calculate the p-values
p_values = []
for score in impact_scores:
ntaxa = len(taxon_weight_pairs)
p_values.append(MAPP.get_p_value(score, ntaxa))
# show the p-values
if fs.show_p_values:
table = [p_values]
row_labels = ['p-value']
col_labels = Codon.g_aa_letters
print >> out, 'p-values:'
print >> out, '<br/>'
print >> out, HtmlTable.get_labeled_table_string(
col_labels, row_labels, table)
print >> out, '<br/><br/>'
# write the html footer
print >> out, '</body>'
print >> out, '</html>'
# return the response
return out.getvalue()
def sendReport(resultList, allGood, testtype):
if allGood:
title = "ALL PASSED:"
else:
title = "SOMETHING WRONG:"
if (testtype == "day"):
title += "Everyday smoke test of CID for namespace: "
title += config.cid_namespaces_asr[0] + "," + config.cid_namespaces_asr[
1] + "," + config.cid_namespaces_tps[
0] + " and " + config.cid_namespaces_tps[1]
else:
title += "Everyhour smoke test of CID for namespace: "
title += config.cid_namespaces_asr[1] + "," + config.cid_namespaces_tps[
0] + " and " + config.cid_namespaces_tps[1]
#title += config.cid_namespaces_asr[0]+","+config.cid_namespaces_asr[1]+","+config.cid_namespaces_tps[0]+" and "+config.cid_namespaces_tps[1]
#title += config.cid_namespaces_asr[1]+","+config.cid_namespaces_tps[0]+" and "+config.cid_namespaces_tps[1]
dateTimeObj = datetime.now()
timestampStr = dateTimeObj.strftime("%Y-%m-%d %H:%M:%S")
html = report.formatTextH3("timestamp: " +
helper.getLocalTime(timestampStr) +
" " + "context:" +
config.context)
titleList, formats = titlesResults()
html2 = htmlTable.table(titleList, formats, resultList,
"cid smoke test results")
html += "<br/><br/>"
html += html2
html += "<br/><br/>"
#html+= PodStatus(config.cid_namespaces_asr[0],config.cid_namespaces_asr[1])
if (testtype == "day"):
html += PodStatus(config.cid_namespaces_asr[0],
config.cid_namespaces_asr[1])
else:
html += PodStatus(config.cid_namespaces_asr[1], None)
html += "<br/><br/>"
if (testtype == "day"):
caption = "CPU Memory of namespace: " + config.cid_namespaces_asr[0]
html1, pods_asr1 = cpuMem(config.cid_namespaces_asr[0], caption)
html += html1
caption = "CPU Memory of namespace: " + config.cid_namespaces_asr[1]
html1, pods_asr1 = cpuMem(config.cid_namespaces_asr[1], caption)
html += html1
html += "<br/>"
html += "<br/><br/>"
if (testtype == "day"):
html += PodStatus(config.cid_namespaces_tps[0],
config.cid_namespaces_tps[1])
else:
html += PodStatus(config.cid_namespaces_tps[1], None)
html += "<br/>"
if (testtype == "day"):
caption = "CPU Memory of namespace: " + config.cid_namespaces_tps[0]
html1, pods_tps1 = cpuMem(config.cid_namespaces_tps[0], caption)
html += html1
html += "<br/>"
caption = "CPU Memory of namespace: " + config.cid_namespaces_tps[1]
html2, pods_tps2 = cpuMem(config.cid_namespaces_tps[1], caption)
html += html2
attached_files = []
if checkContainerStatus(html):
allGood = False
title = "pod status is wrong;" + title
html = replacePodStatus(html)
if (testtype == "day"):
'''indexImg=0
run_id='0000'
podFilenames = granfanaImages(run_id, config.cid_namespaces_asr[1], pods_asr1)
html3, indexImg, attached_files = tableGranfana(run_id, podFilenames, indexImg, attached_files)
if(len(attached_files)>1):
html+="<br/><br/>"
html+=report.formatTextH3("Grafana images of namespace: "+config.cid_namespaces_asr[1]);
html+=html3
#podFilenames = granfanaImages(run_id,config.cid_namespaces_asr[1],pods_asr2)
#html4, indexImg, attached_files = tableGranfana(run_id, podFilenames, indexImg, attached_files)
#html+="<br/>"
#html+=report.formatTextH3("Grafana images of namespace: "+config.cid_namespaces_asr[1]);
#html+=html4
'''
receivers = config.cid_email_receivers
print(html)
email.sendHtmlEmailReceivers(title, html, attached_files, receivers)
else:
if (allGood):
receivers = config.cid_email_receivers_passed
else:
receivers = config.cid_email_receivers
email.sendHtmlEmailReceivers(title, html, attached_files, receivers)
for filename in attached_files:
os.remove(filename)
print(html)
return html