class TestAnalysis(object):
"""
Test utilities in analysis.
"""
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_cumulative.png')
def test_cumulative(self):
np.random.seed(0)
data = np.random.rand(1000)
fig = plt.figure(figsize=(25, 25))
ax00 = plt.subplot2grid((2, 2), (0, 0), fig=fig)
ax01 = plt.subplot2grid((2, 2), (0, 1), fig=fig)
ax10 = plt.subplot2grid((2, 2), (1, 0), fig=fig)
ax11 = plt.subplot2grid((2, 2), (1, 1), fig=fig)
raw, y, x = ra.cumulative(data)
ax00.plot(x, y)
ax00.set_title('cumulative')
raw, y, x = ra.cumulative(data, cumulative=0)
ax01.plot(x, y)
ax01.set_title('non-cumulative')
raw, y, x = ra.cumulative(data, cumulative=-1)
ax10.plot(x, y)
ax10.set_title('reverse-cumulative')
raw, y, x = ra.cumulative(data)
ax11.plot(x, raw)
ax11.set_title('raw data')
plt.tight_layout()
return fig
class TestFragments(object):
"""
Test usage of the FragmentFrame component.
"""
def setup_method(self, method):
self.dirpath = os.path.join(os.path.dirname(__file__), '..', 'data')
self.frag3 = os.path.join(self.dirpath, 'wauto.200.3mers.gz')
self.frag3q = os.path.join(self.dirpath, 'wauto.200.3mers.qual.gz')
self.frag9 = os.path.join(self.dirpath, 'wauto.200.9mers.gz')
self.frag9q = os.path.join(self.dirpath, 'wauto.200.9mers.qual.gz')
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_fragment_profiles.png')
def test_quality_plot(self):
df3 = parse_rosetta_fragments(self.frag3).sample_top_neighbors()
df9 = parse_rosetta_fragments(self.frag9)
# auto-load
df3 = df3.add_quality_measure(None)
# load target quality file
with pytest.raises(ValueError):
df9 = df9.add_quality_measure(self.frag3q)
df9 = df9.add_quality_measure(self.frag9q)
assert df3.is_comparable(df9) is False
assert 'rmsd' in df3
assert 'rmsd' in df9
consensus_seq = df9.select_quantile().quick_consensus_sequence()
consensus_sse = df9.select_quantile(
).quick_consensus_secondary_structure()
assert consensus_seq == "KIPVPVVVNGKIVAVVVVPPENLEEALLEALKELGLIKDPEEVKAVVVSPDGRLELSF"
assert consensus_sse == "EEEEEEEELLEEEEEEEELLLLHHHHHHHHHHHHLLLLLLLLLLEEEEELLLEEEEEE"
fig = plt.figure(figsize=(25, 10))
plot_fragment_profiles(fig, df3, df9, consensus_seq, consensus_sse)
plt.tight_layout()
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='add_fragments_replace.png')
def test_add_fragments_replace(self):
df = parse_rosetta_fragments(self.frag3, source='testfrags')
xx = df[(df['frame'] <= 10) & (df['neighbor'] <= 100)]
dfrep = df.add_fragments(xx, 10)
fig = plt.figure(figsize=(25, 10))
ax0 = plt.subplot2grid((2, 1), (0, 0))
prange = range(len(dfrep.groupby('frame')))
ax0.bar(prange,
[max(y['neighbor']) for x, y in dfrep.groupby('frame')])
ax1 = plt.subplot2grid((2, 1), (1, 0))
ax1.bar(
prange,
[y['neighbors'].unique()[0] for x, y in dfrep.groupby('frame')])
plt.tight_layout()
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='add_fragments_append.png')
def test_add_fragments_append(self):
df = parse_rosetta_fragments(self.frag3)
xx = df[(df['frame'] <= 10) & (df['neighbor'] <= 100)]
dfrep = df.add_fragments(xx, 10, 'append')
fig = plt.figure(figsize=(25, 10))
ax0 = plt.subplot2grid((2, 1), (0, 0))
prange = range(len(dfrep.groupby('frame')))
ax0.bar(prange,
[max(y['neighbor']) for x, y in dfrep.groupby('frame')])
ax1 = plt.subplot2grid((2, 1), (1, 0))
ax1.bar(
prange,
[y['neighbors'].unique()[0] for x, y in dfrep.groupby('frame')])
plt.tight_layout()
return fig
def test_frequency_matrices_and_networks(self):
df3 = parse_rosetta_fragments(self.frag3)
df9 = parse_rosetta_fragments(self.frag9)
# auto-load
df3 = df3.add_quality_measure(None)
# load target quality file
df9 = df9.add_quality_measure(self.frag9q)
matrix = df3.select_quantile(0.1).make_sequence_matrix()
assert matrix.min().min() == -9
matrix = df9.select_quantile(0.1).make_sequence_matrix(frequency=True)
G = df9.select_quantile(0.1).make_per_position_frequency_network()
Gf = df9.select_quantile(0.1).make_frequency_network()
assert matrix.shape == (58, 20)
assert G.number_of_edges() > Gf.number_of_edges()
value = 1 - G.get_edge_data("0X", "1A")['weight']
assert matrix["A"].values[0] == pytest.approx(value)
n = 6
target = str(n + 1) + "R"
for aa in list("ARNDCQEGHILKMFPSTWYV"):
origin = str(n) + aa
if origin in G:
value = 1 - G.get_edge_data(origin, target)['weight']
assert matrix["R"].values[n] == pytest.approx(value)
def test_concat_fragments(self):
# load fragments
_3mers = parse_rosetta_fragments(self.frag3)
# make chunks
_3mers_1 = _3mers[(_3mers['frame'] >= 9) & (_3mers['frame'] <= 12)]
_3mers_2 = _3mers[(_3mers['frame'] >= 22) & (_3mers['frame'] <= 24)]
_3mers_3 = _3mers[(_3mers['frame'] >= 45) & (_3mers['frame'] <= 46)]
# mix fragments
m = concat_fragments([_3mers_3, _3mers_1, _3mers_2])
# checkpoints
assert len(m) == 5400
assert list(m.drop_duplicates('frame')['frame']) == list(range(1, 10))
f1 = NamedTemporaryFile(delete=False)
f1.close()
nonstrict = write_rosetta_fragments(m, 3, 300, f1.name, False)
f2 = NamedTemporaryFile(delete=False)
f2.close()
isstrict = write_rosetta_fragments(m.renumber(10).top_limit(30),
prefix=f2.name,
strict=True)
assert not parse_rosetta_fragments(nonstrict).is_comparable(
parse_rosetta_fragments(isstrict))
class TestPlotUtils( object ):
"""
Test utilities in plots.
"""
def setup_method( self, method ):
self.dirpath = os.path.join(os.path.dirname(__file__), '..', 'data')
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_titles.png')
def test_plot_titles( self ):
fig = plt.figure(figsize=(10, 10))
grid = (1, 1)
ax00 = plt.subplot2grid(grid, (0, 0), fig=fig)
ax00.plot([1, 2, 3], [1, 2, 3])
ru.add_right_title(ax00, 'right title text', rotation=-90)
ru.add_top_title(ax00, 'top title text')
ru.add_left_title(ax00, 'left title text', rotation=90)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_96wells_blanc.png')
def test_plot_96wells_blanc( self ):
fig, ax = rp.plot_96wells()
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_96wells_color.png')
def test_plot_96wells_color( self ):
np.random.seed(0)
df = pd.DataFrame(np.random.randn(8, 12))
fig, ax = rp.plot_96wells(cdata=df)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_96wells_size.png')
def test_plot_96wells_size( self ):
np.random.seed(0)
df = pd.DataFrame(np.random.randn(8, 12))
fig, ax = rp.plot_96wells(sdata=-df)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_96wells_bool.png')
def test_plot_96wells_bool( self ):
np.random.seed(0)
df = pd.DataFrame(np.random.randn(8, 12))
fig, ax = rp.plot_96wells(bdata=df < 0)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_96wells_all.png')
def test_plot_96wells_all( self ):
np.random.seed(0)
df = pd.DataFrame(np.random.randn(8, 12))
fig, ax = rp.plot_96wells(cdata=df, sdata=-df, bdata=df < 0)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_spr.png')
def test_spr( self ):
df = ri.read_SPR(os.path.join(self.dirpath, 'spr_data.csv.gz'))
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_SPR(df, ax, datacolor='black', fitcolor='red')
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_cd.png')
def test_cd( self ):
df = pd.read_csv(os.path.join(self.dirpath, 'cd.csv'))
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_CD(df, ax)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_cd2.png')
def test_cd_read( self ):
def sampling( m, n ):
return [i * n // m + n // (2 * m) for i in range(m)]
df = ri.read_CD(os.path.join(self.dirpath, 'CD'), prefix='kx8', model='J-815')
assert len(df['bin'].unique()) == 36
assert sampling(5, 35) == [3, 10, 17, 24, 31]
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_CD(df, ax, sample=5)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_cd_chirascan.png')
def test_cd_read_chirascan( self ):
df = ri.read_CD(os.path.join(self.dirpath, 'chirascan_cd.csv'), model='chirascan')
fig = plt.figure(figsize=(15, 15))
grid = (3, 2)
for i, sample in enumerate(sorted(df.keys())):
ax = plt.subplot2grid(grid, (int(i / 2), i % 2), fig=fig)
rp.plot_CD(df[sample], ax, sample=5)
ru.add_top_title(ax, sample)
plt.tight_layout()
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_mals.png')
def test_mals( self ):
df = pd.read_csv(os.path.join(self.dirpath, 'mals.csv'))
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_MALS(df, ax)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_mals2.png')
def test_mals_read( self ):
df = ri.read_MALS(filename=os.path.join(self.dirpath, 'mota_1kx8_d2.csv'),
mmfile=os.path.join(self.dirpath, 'mota_1kx8_d2_mm.csv'))
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_MALS(df, ax)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_tm.png')
def test_thermal_melt( self ):
df = pd.read_csv(os.path.join(self.dirpath, 'thermal_melt.csv'))
fig = plt.figure(figsize=(10, 6.7))
ax = plt.subplot2grid((1, 1), (0, 0))
rp.plot_thermal_melt(df, ax)
return fig
def test_multi_fastq( self ):
indat = {'binder1': {'conc1': os.path.join(self.dirpath, 'cdk2_rand_001.fasq.gz'),
'conc2': os.path.join(self.dirpath, 'cdk2_rand_002.fasq.gz'),
'conc3': os.path.join(self.dirpath, 'cdk2_rand_003.fasq.gz')},
'binder2': {'conc1': os.path.join(self.dirpath, 'cdk2_rand_004.fasq.gz'),
'conc2': os.path.join(self.dirpath, 'cdk2_rand_005.fasq.gz'),
'conc3': os.path.join(self.dirpath, 'cdk2_rand_006.fasq.gz')}}
enrich = {'binder1': ['conc1', 'conc3'],
'binder2': ['conc1', 'conc3']}
bounds = ['GAS', 'PGT']
matches = ['ALKKI']
df = ru.sequencing_enrichment(indat, enrich, bounds, matches)
assert 'binder2_conc1' in df.columns
assert 'binder1_conc3' in df.columns
assert 'enrichment_binder1' in df.columns
assert df.shape == (20, 11)
assert df['enrichment_binder2'].mean() == pytest.approx(1.13, rel=1e-3)
assert df['enrichment_binder1'].max() == pytest.approx(5, rel=1e-3)
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_color_hydrophobicity.png')
def test_color_scheme_hydrophobicity( self ):
df = rc.DesignFrame(pd.read_csv(os.path.join(self.dirpath, 'logo_plot_sequence.csv'),
header=None).rename(columns={0: 'sequence_A'}))
fig, axs = rp.logo_plot(df, "A", refseq=False, font_size=10, hight_prop=2,
colors='HYDROPHOBICITY')
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_color_chemistry.png')
def test_color_scheme_chemistry( self ):
df = rc.DesignFrame(pd.read_csv(os.path.join(self.dirpath, 'logo_plot_sequence.csv'),
header=None).rename(columns={0: 'sequence_A'}))
fig, axs = rp.logo_plot(df, "A", refseq=False, line_break=50, font_size=10, hight_prop=2,
colors="CHEMISTRY")
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_color_charge.png')
def test_color_scheme_charge( self ):
df = rc.DesignFrame(pd.read_csv(os.path.join(self.dirpath, 'logo_plot_sequence.csv'),
header=None).rename(columns={0: 'sequence_A'}))
fig, axs = rp.logo_plot(df, "A", refseq=False, line_break=50, font_size=10, hight_prop=2,
colors="CHARGE")
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_color_custom.png')
def test_color_scheme_custom( self ):
custom = {
'A': '#e6194b', 'C': '#3cb44b', 'D': '#ffe119', 'E': '#ffe119',
'F': '#f58231', 'G': '#911eb4', 'H': '#46f0f0', 'I': '#f032e6',
'K': '#d2f53c', 'L': '#d2f53c', 'M': '#008080', 'N': '#e6beff',
'P': '#aa6e28', 'Q': '#fffac8', 'R': '#800000', 'S': '#aaffc3',
'T': '#808000', 'V': '#ffd8b1', 'W': '#000080', 'Y': '#808080'
}
df = rc.DesignFrame(pd.read_csv(os.path.join(self.dirpath, 'logo_plot_sequence.csv'),
header=None).rename(columns={0: 'sequence_A'}))
fig, axs = rp.logo_plot(df, "A", refseq=False, line_break=50, font_size=10, hight_prop=2,
colors=custom)
return fig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_logo_sse.png')
def test_sse_logo(self):
custom = {
'E': '#0000FF', 'H': '#00FF00', 'L': '#FF0000'
}
ff = os.path.join(self.dirpath, 'input_3ssepred.minisilent.gz')
df = ri.parse_rosetta_file(ff, {'structure': 'A'})
fs = df.structure_bits('A')
fig, axs = rp.logo_plot(fs, "A", refseq=False, line_break=50, font_size=10, hight_prop=2,
colors=custom)
return fig
def test_plot_labels( self ):
plt.plot([random.randint(0, 100) for i in range(100)], label='text1')
plt.plot([random.randint(0, 100) for i in range(100)], label='text2')
ax = plt.gca()
ax.legend()
inilabs = [x.get_text() for x in ax.get_legend().texts]
newlabs = ['text3', 'text4']
ru.edit_legend_text(ax, newlabs, 'lines')
endlabs = [x.get_text() for x in ax.get_legend().texts]
with pytest.raises(IndexError):
ru.edit_legend_text(ax, ['text1', 'text2', 'text3'], 'lines')
plt.close()
assert newlabs == endlabs
assert endlabs != inilabs
def test_colors( self ):
red = [255, 0, 0]
newred = ru.color_variant(red, brightness_offset=1)
assert newred == '#ff0101'
cmap = ru.add_white_to_cmap(color='blue')
assert cmap.name == 'FromWhite'
assert cmap.N == 256
class TestExecutables(object):
"""
Test usage for the stand alone executables.
"""
def setup_method(self, method):
self.dirpath = os.path.join(os.path.dirname(__file__), '..', 'data')
self.silent1 = os.path.join(self.dirpath, 'input_2seq.minisilent.gz')
self.silent2 = os.path.join(self.dirpath, 'input_sse.minsilent.gz')
self.silent3 = os.path.join(self.dirpath, 'input_ssebig.minisilent.gz')
self.silent4 = os.path.join(self.dirpath,
'input_3ssepred.minisilent.gz')
self.fastawt = os.path.join(self.dirpath, 'input_2seq.wt.seq')
self.frag3 = os.path.join(self.dirpath, 'wauto.200.3mers.gz')
self.frag3q = os.path.join(self.dirpath, 'wauto.200.3mers.qual.gz')
self.frag9 = os.path.join(self.dirpath, 'wauto.200.9mers.gz')
self.frag9q = os.path.join(self.dirpath, 'wauto.200.9mers.qual.gz')
@pytest.fixture(autouse=True)
def setup(self, tmpdir):
self.tmpdir = tmpdir.strpath
def test_exe_minisilent_gz(self):
options = Namespace(ifile=self.silent1,
ifiles=None,
force=False,
ofile=os.path.join(self.tmpdir, "minisilent.gz"))
minisilent_main(options)
def test_exe_minisilent(self):
options = Namespace(ifile=self.silent1,
ifiles=None,
force=False,
ofile=os.path.join(self.tmpdir, "minisilent.sc"))
minisilent_main(options)
def test_exe_rename_gz(self):
options = Namespace(ifile=self.silent1,
prefix='test',
force=False,
ofile=os.path.join(self.tmpdir, "renamed.gz"))
rename_main(options)
def test_exe_rename(self):
options = Namespace(ifile=self.silent1,
prefix='test',
force=False,
ofile=os.path.join(self.tmpdir, "renamed.sc"))
rename_main(options)
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_exe_check_mutants_logo.png')
def test_exe_check_mutants_logo(self):
options = Namespace(ifile=self.silent1,
ifiles=None,
ifasta=None,
seqID='B',
ffile=self.fastawt,
ofile=os.path.join(self.tmpdir, 'mutants_'),
iformat='png',
ifont=35)
lfig, afig = check_mutants_main(options)
return lfig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_exe_check_mutants_ali.png')
def test_exe_check_mutants_ali(self):
options = Namespace(ifile=self.silent1,
ifiles=None,
ifasta=None,
seqID='B',
ffile=self.fastawt,
ofile=os.path.join(self.tmpdir, 'mutants_'),
iformat='png',
ifont=35)
lfig, afig = check_mutants_main(options)
return afig
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_exe_fragments.png')
def test_exe_plot_fragments(self):
options = Namespace(fsmall=self.frag3,
qsmall=self.frag3q,
flarge=self.frag9,
qlarge=self.frag9q,
pdb=None,
silent=True,
format='h',
ofile=None)
return fragment_main(options)
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='plot_exe_regplot.png')
def test_exe_regplot(self):
options = Namespace(ifile=self.silent3,
ifiles=None,
x='finalRMSD',
y='score',
title='test plot',
color=0,
xlab='rmsd',
ylab='score',
ylim=[-80, -20],
xlim=[0, 6],
fsize=(20, 20),
silent=True,
ofile=None)
return regplot_main(options)
class TestReadSilentFiles(object):
"""
Test reading silent files.
Checks: apply different description and data retrival logic.
"""
@pytest.fixture(autouse=True)
def setup(self, tmpdir):
self.dirpath = os.path.join(os.path.dirname(__file__), '..', 'data')
self.tmpdir = tmpdir.strpath
@pytest.mark.mpl_image_compare(baseline_dir=baseline_test_dir(),
filename='seq_freq_plot_fasta.png')
def test_fasta(self):
# Test simple read
plain_id_string = "{}|PDBID|CHAIN|SEQUENCE"
plain_ids = [
"2TEP:A", "2TEP:B", "2TEP:C", "2TEP:D", "3TP2:A", "3TP2:B"
]
plain_ids = [plain_id_string.format(_) for _ in plain_ids]
df1 = read_fasta(os.path.join(self.dirpath, "*.fa$"), multi=True)
assert sorted(plain_ids) == sorted(list(df1['description'].values))
assert len(df1['sequence_A'].values[0]) == 236
assert len(df1['sequence_A'].values[-1]) == 229
assert df1.shape == (6, 2)
assert len(df1['sequence_A'].unique()) == 2
# Test expanded read
expand_ids = ["2TEP", "3TP2"]
df2 = read_fasta(os.path.join(self.dirpath, "*.fa$"),
expand=True,
multi=True)
assert sorted(expand_ids) == sorted(list(df2['description'].values))
assert df2.shape == (2, 5)
assert 'sequence_A' in df2
assert 'sequence_B' in df2
assert 'sequence_C' in df2
assert 'sequence_D' in df2
# Test write
all_text = [
">2TEP:A|PDBID|CHAIN|SEQUENCE:A",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKD"
"YDPADGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSV"
"SGAVVKVTVIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS",
">2TEP:B|PDBID|CHAIN|SEQUENCE:A",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKDY"
"DPADGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSVSG"
"AVVKVTVIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS",
">2TEP:C|PDBID|CHAIN|SEQUENCE:A",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKDY"
"DPADGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSVSG"
"AVVKVTVIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS",
">2TEP:D|PDBID|CHAIN|SEQUENCE:A",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKDY"
"DPADGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSVSG"
"AVVKVTVIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS",
">3TP2:A|PDBID|CHAIN|SEQUENCE:A",
"GAMTSWRDKSAKVQVKESELPSSIPAQTGLTFNIWYNKWSQGFAGNTRFVSPFALQPQLHSGKTRGDNDGQLFFCLFFA"
"KGMCCLGPKCEYLHHIPDEEDIGKLALRTEVLDCFGREKFADYREDMGGIGSFRKKNKTLYVGGIDGALNSKHLKPAQI"
"ESRIRFVFSRLGDIDRIRYVESKNCGFVKFKYQANAEFAKEAMSNQTLLLPSDKEWDDRREGTGLLVKWAN",
">3TP2:B|PDBID|CHAIN|SEQUENCE:A",
"GAMTSWRDKSAKVQVKESELPSSIPAQTGLTFNIWYNKWSQGFAGNTRFVSPFALQPQLHSGKTRGDNDGQLFFCLFFA"
"KGMCCLGPKCEYLHHIPDEEDIGKLALRTEVLDCFGREKFADYREDMGGIGSFRKKNKTLYVGGIDGALNSKHLKPAQI"
"ESRIRFVFSRLGDIDRIRYVESKNCGFVKFKYQANAEFAKEAMSNQTLLLPSDKEWDDRREGTGLLVKWAN"
]
assert write_fasta(df1, "A") == "\n".join(all_text) + "\n"
# Pick chains from expanded read
picked_text = [
">2TEP:A",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKDYDPA"
"DGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSVSGAVVKVT"
"VIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS",
">3TP2:A",
"GAMTSWRDKSAKVQVKESELPSSIPAQTGLTFNIWYNKWSQGFAGNTRFVSPFALQPQLHSGKTRGDNDGQLFFCLFFAKGM"
"CCLGPKCEYLHHIPDEEDIGKLALRTEVLDCFGREKFADYREDMGGIGSFRKKNKTLYVGGIDGALNSKHLKPAQIESRIRF"
"VFSRLGDIDRIRYVESKNCGFVKFKYQANAEFAKEAMSNQTLLLPSDKEWDDRREGTGLLVKWAN",
">2TEP:C",
"AETVSFNFNSFSEGNPAINFQGDVTVLSNGNIQLTNLNKVNSVGRVLYAMPVRIWSSATGNVASFLTSFSFEMKDIKDYDPA"
"DGIIFFIAPEDTQIPAGSIGGGTLGVSDTKGAGHFVGVEFDTYSNSEYNDPPTDHVGIDVNSVDSVKTVPWNSVSGAVVKVT"
"VIYDSSTKTLSVAVTNDNGDITTIAQVVDLKAKLPERVKFGFSASGSLGGRQIHLIRSWSFTSTLITTTRRS"
]
assert write_fasta(df2, "AC") == "\n".join(picked_text) + "\n"
# Individual prints
write_fasta(df2,
"A",
filename=os.path.join(self.tmpdir, "singles.fa"),
split=True)
for _ in ["singles_f0001.fa", "singles_f0002.fa"]:
df = read_fasta(os.path.join(self.tmpdir, _))
assert df.shape == (1, 2)
newseq = df["sequence_A"].values[0]
newid = df["description"].values[0]
expected = df2[df2["description"] == newid.split(":")
[0]]["sequence_A"].values[0]
assert expected == newseq
# plot
fig = plt.figure(figsize=(25, 10))
ax = plt.subplot2grid((1, 1), (0, 0))
sequence_frequency_plot(df1,
'A',
ax,
refseq=False,
key_residues='12-35',
clean_unused=0)
return fig
def test_hmm(self):
df = read_hmmsearch(os.path.join(self.dirpath, 'search.hmm.gz'))
assert df.shape[0] == 4932
assert len(df['description'].unique()) == 4927
assert df[df['full-e-value'] < 10].shape[0] == 2650
df = read_hmmsearch(os.path.join(self.dirpath, 'search2.hmm.gz'))
assert df.shape[0] == 11
assert len(df.iloc[0]['sequence']) == 87
df = read_hmmsearch(os.path.join(self.dirpath, 'scan.hmm.gz'))
assert df.shape[0] == 9
assert len(df.iloc[0]['sequence']) == 99
def test_pymol(self):
df = parse_rosetta_file(
os.path.join(self.dirpath, 'input_2seq.minisilent.gz'),
{'sequence': 'B'})
df.add_reference_sequence('B', df.iloc[0].get_sequence('B'))
df = df.identify_mutants('B').head()
pick1 = ""
pick2 = "sele test_3lhp_binder_labeled_00002_mut, test_3lhp_binder_labeled_00002 and " \
"((c. B and (i. 1-2 or i. 7-9 or i. 11-12 or i. 14-17 or i. 19 or i. 21-23 or " \
"i. 25-27 or i. 31-33 or i. 35-39 or i. 42 or i. 45 or i. 48 or i. 52 or " \
"i. 64-68 or i. 70-75 or i. 77 or i. 79-82 or i. 84-86 or i. 88-89 or " \
"i. 91-102 or i. 104-111 or i. 113-116)))"
sel = pymol_mutant_selector(df)
assert len(sel[0]) == 0
assert sel[0] == pick1
assert len(sel[1]) != 0
assert sel[1] == pick2
def test_master(self):
df = parse_master_file(os.path.join(self.dirpath, 'master.search'),
max_rmsd=1.4,
piece_count=2,
shift_0=True)
assert df.rmsd.max() == 1.3967
assert df.shape == (42, 5)
assert df.iloc[-1].match == [[34, 40], [42, 48]]