def make_hist(df, title, num_bins = 8, code_percentile=.99):
'''
Takes data, title, number of bins (max 10), and percentile.
Outputs a histogram.
'''
title = title.title()
data_list = df.dropna().tolist()
top_code_val = df.quantile(code_percentile)
distinct_vals = len(set(data_list))
num_bins = min(distinct_vals, num_bins)
# plt.style.use('ggplot')
# df.hist(bins = np.linspace(0, top_code_val, num_bins + 1), normed=True)
# plt.xlabel(title)
# plt.title('Histogram of ' + title.replace("_", " "))
# plt.tight_layout()
# plt.savefig('Histogram_' + title + '.png', format='png')
# plt.close()
sns.displot(df)
def plot_density_plots(metric):
data = (pd.read_csv(f"../generated_data/data_{metric}.csv").drop(
columns="Unnamed: 0").dropna().rename(columns={"value": "distance"}))
sns.displot(
data=data,
x="distance",
hue="variable",
multiple="stack",
height=6,
aspect=0.7,
)
plt.subplots_adjust(top=0.85)
plt.title("\n".join(
wrap(
f"Distribution of the {metric} distances "
f"among diagnostic categories",
50,
)))
plt.savefig(DOTENV_KEY2VAL["GEN_FIGURES_DIR"] + metric +
"_histogram.png", )
def plot_median_score_distribution(df, title, path, file_name):
if not os.path.exists(path):
os.mkdir(path)
dis_plt = sns.displot(df, x="median_scores", hue="dose", kind="hist",
multiple="stack", palette = 'viridis', height=6.5, aspect=1.7)
dis_plt.fig.suptitle(title)
dis_plt.fig.subplots_adjust(top=.92)
plt.savefig(os.path.join(path, file_name))
plt.show()
def plot_p_value_dist(df, path, file_name):
"""Plot p-value frequency distribution per dose"""
if not os.path.exists(path):
os.mkdir(path)
dis_plt = sns.displot(df, x="p_values", col="dose", col_wrap=3, binwidth=0.03)
dis_plt.fig.suptitle("P-values distribution across all doses(1-6)", size = 16)
dis_plt.fig.subplots_adjust(top=.92)
plt.savefig(os.path.join(path, file_name))
plt.show()
def general_plots():
'''Make plots to illustrate the results of the scRNA-Seq analysis'''
valuetype, use_spikeins, biotype_to_use = "Tpms", False, "protein_coding"
adata, phases = read_counts_and_phases(valuetype, use_spikeins,
biotype_to_use)
# QC plots before filtering
sc.pl.highest_expr_genes(adata, n_top=20, show=False, save=True)
shutil.move("figures/highest_expr_genes.pdf",
f"figures/highest_expr_genes_AllCells.pdf")
# Post filtering QC
do_log_normalization = True
do_remove_blob = False
adata, phasesfilt = qc_filtering(adata, do_log_normalization,
do_remove_blob)
sc.pp.highly_variable_genes(adata,
min_mean=0.0125,
max_mean=3,
min_disp=0.5)
sc.pl.highly_variable_genes(adata, show=False, save=True)
shutil.move("figures/filter_genes_dispersion.pdf",
f"figures/filter_genes_dispersionAllCells.pdf")
# UMAP plots
# Idea: based on the expression of all genes, do the cell cycle phases cluster together?
# Execution: scanpy methods: UMAP statistics first, then make UMAP
# Output: UMAP plots
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)
sc.tl.umap(adata)
plt.rcParams['figure.figsize'] = (10, 10)
sc.pl.umap(adata, color=["phase"], show=False, save=True)
shutil.move("figures/umap.pdf", f"figures/umapAllCellsSeqCenterPhase.pdf")
# General display of RNA abundances in TPMs
sbn.displot(np.concatenate(adata.X), color="tab:orange")
plt.xlabel("TPM")
plt.ylabel("Density")
plt.savefig("figures/rna_abundance_density.pdf")
# plt.show()
plt.close()
def _plot_metric_nodes_distribution(self,
leaf_metrics: pd.DataFrame,
dist_type: str = 'kde') -> None:
g = sns.displot(data=leaf_metrics,
x=self.metric_col,
hue=self.__NODE_RULES__COL,
kind=dist_type,
common_norm=False)
g.savefig(
f"bias_distribution_{self.metric_col}-{self.dataset_name}.png",
dpi=600)
plt.show()
def distToCentroid(self, labels, distances):
"""
Method to compute and plot distance of each point to the centroïd of its cluster
input
labels: list containing cluster label attached to index
distances: distance from point to centroïd
output
distribution plot of distances from each point to its cluster's centroïd
"""
self.clustersPCA = pd.DataFrame([list(i) for i in zip(labels,distances)],columns=['cluster','distance'])
self.clustersPCA['distanceToCluster'] = self.clustersPCA['distance'].apply(lambda x: min(x))
self.clustersPCA['distToCluster1'] = self.clustersPCA['distance'].apply(lambda x: x[0])
self.clustersPCA['distToCluster2'] = self.clustersPCA['distance'].apply(lambda x: x[1])
self.clustersPCA['distToCluster3'] = self.clustersPCA['distance'].apply(lambda x: x[2])
self.clustersPCA.cluster.replace({0:1, 1:2, 2:3}, inplace=True)
sns.displot(data=self.clustersPCA, x='distanceToCluster', hue='cluster', kde=True)
plt.show()
def plot_island_distribution(data: PlotData, island_size_: int, tmp_dir: Path):
csv_out = Path(tmp_dir, f"out_{island_size_}.csv")
with time_func(f"Populating the CSV at {csv_out}"):
populate_csv(csv_out, data.distributions, [island_size_])
with time_func("Reading the CSV"):
data_set = pd.read_csv(csv_out)
for normalize in (True, False):
xs = "avg_occurr" if not normalize else "ln_avg_occurr"
with time_func("Displaying the dataset:"):
sns.displot(
data_set,
x=xs,
hue="edge_length",
kind="kde", # kde=True,
palette=sns.color_palette("Paired", data.lambdas))
title = f"island_size_{island_size_}"
if normalize:
title = "normalized_" + title
out_fie = Path(data.out_dir, f"{title}.png")
plt.title(title)
plt.savefig(str(out_fie))
def map_total_calc_cov(target_moment_df,
col_name,
title,
file_path: str = None):
# 過去に保存した同じファイルがあれば削除
if path.exists(file_path):
os.remove(file_path)
sns_plt = sns.displot(data=target_moment_df, x=col_name)
plt.title(title)
sns_plt.savefig(file_path)
def analysis(request):
data = pd.read_csv(
r"C:/Users/Ajay's/Desktop/Prediction/HousePricePrediction/ml/static/js/USA_Housing.csv"
)
sns.displot(data=data,
x="Price",
y="Avg. Area Number of Rooms",
kind="kde",
rug=True)
plt.savefig(
r"C:/Users/Ajay's/Desktop/Prediction/HousePricePrediction/ml/static/img/price_and_room.png"
)
sns.displot(data=data, x="Price", y="Avg. Area House Age", kind="kde")
plt.savefig(
r"C:/Users/Ajay's/Desktop/Prediction/HousePricePrediction/ml/static/img/price_and_house.png"
)
data = data.drop(['Address'], axis=1)
X = data.drop('Price', axis=1)
Y = data['Price']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.30)
model = LinearRegression()
model.fit(X_train, Y_train)
coeff_df = pd.DataFrame(model.coef_, X.columns, columns=['Cofficient'])
v1 = round(coeff_df.at['Avg. Area Income', 'Cofficient'], 3)
v2 = round(coeff_df.at['Avg. Area House Age', 'Cofficient'], 3)
v3 = round(coeff_df.at['Avg. Area Number of Rooms', 'Cofficient'], 3)
v4 = round(coeff_df.at['Avg. Area Number of Bedrooms', 'Cofficient'], 3)
v5 = round(coeff_df.at['Area Population', 'Cofficient'], 3)
prediction = model.predict(X_test)
error = round(np.sqrt(metrics.mean_absolute_error(Y_test, prediction)), 3)
return render(request, 'dash.html', {
"v1": v1,
"v2": v2,
"v3": v3,
"v4": v4,
"v5": v5,
"error": error,
})
def plot_congestion_dist(columns, dataframe, path, prefix, save, show):
for atr in columns:
plt.figure(figsize=set_size(418))
plt.style.use('seaborn')
plt.rcParams.update(tex_fonts)
plt.title('Distribution of ' + atr)
plt.ylabel('Count')
sns.displot(x=atr, data=dataframe, palette='Spectral')
if atr is 'TempExMax':
plt.xlabel('Maximum temporal extend [min]')
elif atr is 'SpatExMax':
plt.xlabel('Maximum spatial extend [m]')
elif atr is 'TempDist':
plt.xlabel('Minimum temporal distance to incident [min]')
elif atr is 'SpatDist':
plt.xlabel('Minimum spatial distance to incident [m]')
elif atr is 'temporalGlobalLoc':
plt.xlabel('Relative temporal location')
elif atr is 'spatialGlobalLoc':
plt.xlabel('Relative spatial location')
elif atr is 'temporalInternalLoc':
plt.xlabel('Internal relative temporal location')
elif atr is 'spatialInternalLoc':
plt.xlabel('Internal relative spatial location')
elif atr is 'Coverage':
plt.xlabel('Ratio of jammed cells in covering rectangle [\%]')
elif atr is 'TimeLossCar':
plt.xlabel('Time loss per Cars [s]')
elif atr is 'TimeLossHGV':
plt.xlabel('Time loss per HGVs [s]')
else:
plt.xlabel(atr)
if save:
plt.savefig(path + prefix + '_congestion_dist_' + atr + '.pdf')
if not show:
plt.close()
if show:
plt.show()
else:
plt.close()
def hist_pair2(df, stat, col2, cum=False, title=None):
"""Histogram of population pair stats.
dd12_{n}_{k}_pop{kj}
ddRank12_{n}_{k}_pop{kj}
Parameters
----------
df : TYPE
DESCRIPTION.
stat : TYPE
DESCRIPTION.
col2 : TYPE
DESCRIPTION.
cum : TYPE, optional
DESCRIPTION. The default is False.
title : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
stat_cols = [col for col in df.columns if stat == col.split("_")[0]]
df_stat = pd.melt(df.filter(regex=f"{stat}"),
value_vars=stat_cols,
var_name=stat,
value_name=col2)
# add pop column
pop = df_stat[stat].str.split("_", n=-1, expand=True)
df_stat["pops"] = pop[pop.columns[-1]]
df_stat["subpop"] = pop[2]
df_stat[stat] = pop[0]
# plotting if just obs or just sims
if title is None:
title = stat
name = ""
if cum:
g = sns.histplot(data=df_stat,
x=col2,
hue="subpop",
col="pops",
kind="ecdf")
name = "cum"
else:
g = sns.displot(data=df_stat,
x=col2,
hue="subpop",
col="pops",
kind="hist")
g.savefig(f"{stat}.{name}histpair.pdf", bbox_inches='tight')
def kolm_test(file, pref, source, target, meta_file):
"""
Performs KS test for optimization process
Args:
file (str): Path to the file with TopoCMap output table
pref (str): Prefix to all statistical files (e.g. histograms etc.)
source (str): Source cell type
target (str): Target cell type
meta_file (str): Path to the file with drugs metadata
Returns:
statistics (:obj:`list` of :obj:`tuple` of :obj:`float`): Output of ks_2samp function for all 10 iterations
mean_1 (float): Mean of 'Golden Standard' molecules distribution
mean_2 (float): Mean of means of all molecules distribution
"""
dist = []
cids = []
cmap_db = pd.read_csv(file)
drug_meta = pd.read_csv(meta_file)
cids_cur = stand_chems(source, target)
cids_cur = [float(cid) for cid in cids_cur]
pert_cur = []
cids_cur = pd.unique(cids_cur)
for ind, chem in enumerate(drug_meta['pubchem_cid']):
for chem_1 in cids_cur:
try:
if int(chem) == int(chem_1):
pert_cur.append(drug_meta['pert_id'].loc[ind])
except ValueError:
continue
for ind, chem in enumerate(cmap_db['pert_id']):
for chem_1 in pert_cur:
if chem == chem_1:
cids.append(chem)
dist.append(cmap_db['cosine_dist'].loc[ind])
statistics = []
mean_1 = np.mean(dist)
means = []
cmap_db = cmap_db[~cmap_db["pert_id"].isin(pert_cur)]
print(len(cmap_db))
for i in range(10):
dist_rand = np.random.choice(list(cmap_db['cosine_dist']), len(dist))
means.append(np.mean(dist_rand))
stat, pval = stats.ks_2samp(dist, dist_rand)
statistics.append((stat, pval))
sns_plot = sns.displot([dist, dist_rand], kde=True)
sns_plot.savefig(pref + str(i) + ".png")
mean_2 = np.mean(means)
return statistics, mean_1, mean_2
def plot_2d_kde(x, y, hue, data):
"""
Plot a bivariate kernel density estimate
Parameters
----------
x : array
x-axis variable
y : array
y-axis variable
hue : array
Variable to map to colors in order to visually distinguish separate bivariate densities
Returns
-------
None
"""
plt.figure(figsize=(16, 16))
sns.displot(x=x, y=y, hue=hue, kind='kde', data=data)
plt.show()
def plot_cont(df, plt_typ):
numeric_columns = df.select_dtypes(include=['number']).columns.tolist()
df = df[numeric_columns]
for i in range(0, len(numeric_columns), 2):
if len(numeric_columns) > i + 1:
plt.figure(figsize=(10, 4))
plt.subplot(121)
if plt_typ == 'boxplot':
sns.boxplot(df[numeric_columns[i]])
plt.subplot(122)
sns.boxplot(df[numeric_columns[i + 1]])
elif plt_typ == 'displot':
sns.boxplot(df[numeric_columns[i]])
plt.subplot(122)
sns.displot(df[numeric_columns[i + 1]])
else:
print('Pass either distplot/boxplot')
plt.tight_layout()
plt.show()
def GCdistribut(self, _indf, out, X='query_length', Dup=[], log=False, title=''):
if not _indf.empty:
indef = _indf.copy()
if Dup:
indef = indef[Dup +[X]].drop_duplicates(keep='first')
indef[X] = indef[X].astype(float)
dp = sns.displot(data=indef, x=X, kde=True, log_scale=log)
dp.set_xticklabels(rotation=270)
if title:
plt.title(title)
plt.tight_layout()
plt.savefig( out )
plt.close()
def create_fig(data, draw=False):
if draw is not False:
df = pd.DataFrame(data)
df.columns = ['Node', 'Edges']
fig = sns.displot(df['Edges'], bins=max(df['Edges']) + 1, kde=True)
plt.title('Amount of edges per node')
plt.show()
else:
pass
def DerCore_Ratio(enh):
cdratio = enh.loc[enh["core_remodeling"] == 1].groupby(
['enh_id', 'core'])["arch"].count().reset_index()
cdratio.columns = ["enh_id", "core", "num derived regions per enh"]
data = cdratio.loc[cdratio["core"] == 0].describe()
sns.set("talk")
g = sns.displot(cdratio.loc[cdratio["core"] == 0,
"num derived regions per enh"],
kind="ecdf")
outf = f"{RE}cdf_n_der.pdf"
plt.savefig(outf, bbox_inches="tight")
return data
def show_sentence_length():
train_data['sentence_length'] = list(
map(lambda x: len(x), train_data['sentence']))
##绘制训练集句子长度分布
sns.countplot(train_data["sentence_length"])
plt.xticks([])
plt.show()
# 绘制dist长度分布图
sns.displot(train_data["sentence_length"])
plt.yticks([])
plt.show()
valid_data['sentence_length'] = list(
map(lambda x: len(x), valid_data['sentence']))
##绘制训练集句子长度分布
sns.countplot(valid_data["sentence_length"])
plt.xticks([])
plt.show()
# 绘制dist长度分布图
sns.displot(valid_data["sentence_length"])
plt.yticks([])
plt.show()
def _plot_univariate(y_test, y_sampled):
idlist = [[i + 1] * len(x) for i, x in enumerate([y_test, y_sampled])]
df = pd.DataFrame(np.array(
[np.concatenate([y_test, y_sampled]),
np.concatenate(idlist)]).T,
columns=['value', 'type'])
df['type'] = df['type'].map({1: 'original', 2: 'sampled'})
return sns.displot(df,
x='value',
rug=True,
kind='kde',
color='black',
hue='type')
def get_charging_time(self):
# Plot pdf do tempo de recarga do ev
sns.set_theme(style="darkgrid")
# Transformando vetor de curvas em vetor de horas
hour = 1
nem_charging_time = []
for item in self.ev_charging_time:
if item > 0:
aux = [hour] * abs(int(item))
nem_charging_time.extend(aux)
hour += 1
if hour == 25:
hour = 1
ev_charging_dict = {"value": nem_charging_time}
df = pd.DataFrame.from_dict(ev_charging_dict)
sns.displot(data=df, x="value", kind="kde")
plt.gcf().subplots_adjust(bottom=0.15)
plt.xlabel('Time [h]')
plt.xlim(0, 24)
plt.ylabel('PDF')
plt.savefig("plot_charging_time.png", dpi=199)
plt.show()
def plot_distance_from_median_pl(distance_files, patient_types):
print(f"-------- Distance from median PL --------")
distance_stats_df = pd.DataFrame()
for distances, patient_type in zip(distance_files, patient_types):
distances = pd.read_csv(DOTENV_KEY2VAL["GEN_DATA_DIR"] + distances)
distances = distances.set_index("Unnamed: 0")
for i in range(len(HOMOLOGY_DIMENSIONS)):
distance_data = distances.iloc[:, i]
print(f"-------- {patient_type} H_{i} --------")
distance_stats_dict = dict()
distance_stats_dict["Mean"] = np.mean(distance_data)
distance_stats_dict["Median"] = np.median(distance_data)
distance_stats_dict["Standard deviation"] = np.std(distance_data)
distance_stats_dict["Q3"] = np.quantile(distance_data, 0.75)
distance_stats_dict["Max"] = np.max(distance_data)
# distance_stats_dict["kurtosis"] = kurtosis(distance_data)
distance_stats_dict["Skewness"] = skew(distance_data)
distance_stats_dict["Shapiro-Wilk test"] = shapiro(
distance_data
).pvalue
print(f"Shapiro-Wilk: {shapiro(distance_data)}")
distance_stats_df_entry = pd.DataFrame.from_dict(
distance_stats_dict, orient="index"
)
distance_stats_df_entry.columns = [f"{patient_type} $H_{i}$"]
distance_stats_df = distance_stats_df.append(
distance_stats_df_entry.T
)
ax = sns.displot(distance_data, kde=True)
# ax.set(ylim=(0, 1)) # Finetuned to the data
ax.set(xlim=(0, 12))
plt.savefig(
DOTENV_KEY2VAL["GEN_FIGURES_DIR"]
+ "/median_pls/"
+ f"median_pl_{patient_type}_H_{i}_displot.png",
bbox_inches="tight",
)
test_results = pd.DataFrame(
distance_stats_df["Shapiro-Wilk test"]
).applymap(lambda x: format_tex(x))
stats = distance_stats_df[
["Mean", "Median", "Standard deviation", "Q3", "Max", "Skewness"]
].applymap(lambda x: format_tex_numbers(x))
distance_stats_df = stats.join(test_results)
print(test_results)
distance_stats_df.to_latex(
DOTENV_KEY2VAL["GEN_DATA_DIR"]
+ "distance_from_median_pl_statistics.tex",
float_format="{:0.2f}".format,
escape=False,
)
def Exploration_Taille_Masques():
df_train = cheminSources + dftrain
st.title("Taille des masques de forme\n")
st.write('\n')
st.subheader("Répartition des surfaces (en pixels) des masques encodés :")
file2 = df_train
df_train = load_data(file2)
sns.histplot(df_train[df_train['nb_pixels'] != 0].nb_pixels,
bins=20,
kde=True,
stat="density")
st.pyplot()
nb_form = df_train[df_train['nb_pixels'] != 0].shape[0]
st.subheader(
"Répartition des surfaces (en pixels) des masques encodés suivant leur label : "
)
df_form = df_train[(df_train['nb_pixels'] != 0)]
sns.set_context(font_scale=2)
sns.displot(data=df_form,
bins=20,
x='nb_pixels',
col="Label",
stat="density")
st.pyplot()
st.subheader("Boxplot associés :")
sns.boxplot(
y='nb_pixels',
x='Label',
data=df_form,
#width=0.5,
palette="colorblind")
st.pyplot()
def ETC2Run():
delta_vals = [i * 0.04 for i in range(1, 25)]
ub_regrets = []
actual_regrets = []
num_runs = 500
for delta in delta_vals:
ub_regret_sum, actual_regret_sum = 0, 0
for run_no in range(num_runs):
ub_regret, actual_regret = ExploreThenCommit2(n=1000, delta=delta)
ub_regret_sum += ub_regret
actual_regret_sum += actual_regret
ub_regrets.append(ub_regret_sum / num_runs)
actual_regrets.append(actual_regret_sum / num_runs)
plt.plot(delta_vals, ub_regrets, label="Upper Bound")
plt.plot(delta_vals, actual_regrets, label="Actual Regret")
plt.legend()
plt.show()
m_vals = [i * 15 for i in range(1, 25)]
actual_regrets = np.zeros((len(m_vals), num_runs))
for i, m in enumerate(m_vals):
actual_regret_sum = 0
for run_no in range(num_runs):
_, actual_regret = ExploreThenCommit2(n=2000, delta=0.1, m=m)
actual_regrets[i, run_no] = actual_regret
plt.plot(m_vals, actual_regrets.mean(axis=1))
plt.show()
plt.plot(m_vals, actual_regrets.std(axis=1))
plt.show()
hue = np.array(m_vals)
hue = np.repeat(hue, num_runs)
sns.displot(x=actual_regrets.flatten(), hue=hue, kind="kde")
plt.show()
def plot_input_length(df, split_folder):
"""
Plots the input length of the decisions in the given dataframe
:param df: the dataframe containing the decision texts
:param split_folder: where to save the plots and csv files
:return:
"""
# compute median input length
input_length_distribution = df[['num_tokens_spacy', 'num_tokens_bert']].describe().round(0).astype(int)
input_length_distribution.to_csv(split_folder / 'input_length_distribution.csv', index_label='measure')
# bin outliers together at the cutoff point
cutoff = 4000
cut_df = df[['num_tokens_spacy', 'num_tokens_bert']]
cut_df.num_tokens_spacy = cut_df.num_tokens_spacy.clip(upper=cutoff)
cut_df.num_tokens_bert = cut_df.num_tokens_bert.clip(upper=cutoff)
hist_df = pd.concat([cut_df.num_tokens_spacy, cut_df.num_tokens_bert], keys=['spacy', 'bert']).to_frame()
hist_df = hist_df.reset_index(level=0)
hist_df = hist_df.rename(columns={'level_0': 'tokenizer', 0: 'Number of tokens'})
plot = sns.displot(hist_df, x="Number of tokens", hue="tokenizer",
bins=100, kde=True, fill=True, height=5, aspect=2.5, legend=False)
plot.set(xticks=list(range(0, 4500, 500)))
plt.ylabel('Number of court cases')
plt.legend(["BERT", "SpaCy"], loc='upper right', title='Tokenizer', fontsize=16, title_fontsize=18)
plot.savefig(split_folder / 'input_length_distribution-histogram.png', bbox_inches="tight")
plt.clf()
plot = sns.displot(hist_df, x="Number of tokens", hue="tokenizer", kind="ecdf", legend=False)
plt.ylabel('Number of court cases')
plt.legend(["BERT", "SPaCy"], loc='lower right', title='Tokenizer')
plot.savefig(split_folder / 'input_length_distribution-cumulative.png', bbox_inches="tight")
plt.clf()
plot = sns.displot(cut_df, x="num_tokens_spacy", y="num_tokens_bert")
plot.savefig(split_folder / 'input_length_distribution-bivariate.png', bbox_inches="tight")
plt.clf()
def distributions(
df: pd.DataFrame,
dist_class: str,
column: str,
show: bool,
save_location: str,
) -> None:
"""
Plot distribution of the same x variable for multiple classes
Args:
df (pd.DataFrame): Data
dist_class (str): How to split the values from df
column (str): Column of dataframe
show (bool): Flag to show plot
hist (bool): Hist type flag
kde (bool): Kde type flag
save_location (str): Path to where the plot should be saved
"""
sns.displot(x=df[column], hue=df[dist_class], kind="kde", clip=(1.0, 8.0))
plt.savefig(save_location)
if show:
plt.show()
def make_perc_coverred_dist(self):
"""
from all summary, plot perc covered by sequencing data on bar plot
For All, HIP, Supp-SGD and Supp-PROT
:return:
"""
print(self._all_summary.columns)
print(self._all_mut.columns)
not_fully_covered = self._all_summary.loc[
(self._all_summary["aligned_perc"] < 1)
& (self._all_summary["found"] == "y")]
print(self._all_summary["aligned_perc"])
print(not_fully_covered.shape)
fig, ax = plt.subplots(figsize=(10, 12))
sns.displot(not_fully_covered.aligned_perc * 100,
bins=40,
ax=ax,
color="#084c61",
edgecolor="#084c61")
plt.title("Human 9.1 ORFs")
plt.xlabel("Percent of ORF len aligned")
plt.tight_layout()
plt.savefig(os.path.join(self._dir, "nfully_human91_perc_dist.png"))
def displot(data, key, aim, **kwargs):
sns.set_style('white')
# fig = plt.figure(figsize=(10, 7.5))
fig = plt.figure()
ax = sns.displot(data=data, x=aim, kind="ecdf", hue="algorithm",
hue_order=['DRPA', 'FP', 'WMMSE', 'maximum', 'random'],
height=3, aspect=1.5, facet_kws=dict(legend_out=False),
# aspect=1.5, facet_kws=dict(legend_out=False),
**kwargs)
ax.legend.set_title('')
ax.legend._loc=7
plt.xlabel(f'Average {aim} (bps/Hz)')
plt.grid(axis="y")
return fig, ax
def oneVarDistribution(xName, data, title, catName=""):
if is_numeric_dtype(data.loc[:, xName]):
if catName == "":
preparation()
sns.displot(data=data, x=xName)
plt.title(title)
plt.show()
else:
preparation()
sns.displot(data=data, x=xName, hue=catName)
plt.title(title)
plt.show()
else:
if catName == "":
preparation()
sns.countplot(data=data, x=xName)
plt.title(title)
plt.show()
else:
preparation()
sns.countplot(data=data, x=xName, hue=catName)
plt.title(title)
plt.show()
def plot_correlation_distribution(countries,
delta=timedelta(days=60),
weekly=True):
"""
Args:
countries ():
delta ():
weekly ():
"""
# regions
x = None
regions = []
if 'CZ' in countries: regions.append(CZ_regions)
if 'PL' in countries: regions.append(PL_regions)
if 'SE' in countries: regions.append(SE_regions)
if 'IT' in countries: regions.append(IT_regions)
# compute correlations
for country in regions:
components = 'ID' if country[0][:2] in {'PL', 'SE'} else 'IRD'
corrs = prediction_data_correlation(country,
components,
delta=delta,
weekly=weekly)
corrs['Country'] = country[0][:2]
if x is None: x = corrs
else: x = pd.concat([x, corrs])
# plot
fig, ax = plt.subplots(figsize=(8, 6))
sns.displot(x,
x="D",
hue="Country",
element="step",
multiple="stack",
bins=20,
ax=ax)
ax.set_xlim([-1, 1])
def plot_prob_dist(data, output_path, cluster_type, n_cluster):
"""
KDE plot of cluster probabilities.
Args:
data: Neuron activation dataframe with cluster labels
output_path: Output path to save to
cluster_type: Cluster algorithm used for file naming
n_cluster: Number of unique clusters in clustering algorithm
Returns:
"""
plt.figure(figsize=(12, 6))
sns.displot(data=data,
x='label prob',
hue='label',
multiple='stack',
palette='dark',
kind='kde',
aspect=2)
plt.xlabel('Cluster Probability', fontsize=14)
plt.ylabel('Density', fontsize=14)
plt.savefig(join(output_path, f'{cluster_type}_{n_cluster}_prob_dist.png'),
bbox_inches='tight')
def confoundplot(tseries, gs_ts, gs_dist=None, name=None, normalize=True,
units=None, tr=None, hide_x=True, color='b', nskip=0,
cutoff=None, ylims=None):
# Define TR and number of frames
notr = False
if tr is None:
notr = True
tr = 1.
ntsteps = len(tseries)
# Normalize time series
tseries = np.array(tseries)
if normalize:
tseries /= tr
# Define nested GridSpec
gs = mgs.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_ts,
width_ratios=[1, 100], wspace=0.0)
ax_ts = plt.subplot(gs[1])
ax_ts.grid(False)
ax_ts.plot(tseries, color=color)
ax_ts.set_xlim((0, ntsteps - 1))
# Set 10 frame markers in X axis
interval = ntsteps // 10
xticks = list(range(0, ntsteps)[::interval]) + [ntsteps - 1]
ax_ts.set_xticks(xticks)
if not hide_x:
if notr:
ax_ts.set_xlabel('time (frame #)')
else:
ax_ts.set_xlabel('time (s)')
labels = tr * np.array(xticks)
ax_ts.set_xticklabels(['%.02f' % t for t in labels.tolist()])
else:
ax_ts.set_xticklabels([])
no_scale = notr or not normalize
if not name is None:
var_label = name
if not units is None:
var_label += (' [{}]' if no_scale else ' [{}/s]').format(units)
ax_ts.set_ylabel(var_label)
for side in ["top", "right"]:
ax_ts.spines[side].set_color('none')
ax_ts.spines[side].set_visible(False)
if not hide_x:
ax_ts.spines["bottom"].set_position(('outward', 20))
ax_ts.xaxis.set_ticks_position('bottom')
else:
ax_ts.spines["bottom"].set_color('none')
ax_ts.spines["bottom"].set_visible(False)
ax_ts.spines["left"].set_position(('outward', 30))
ax_ts.yaxis.set_ticks_position('left')
# Calculate Y limits
def_ylims = [0.95 * tseries[~np.isnan(tseries)].min(),
1.1 * tseries[~np.isnan(tseries)].max()]
if ylims is not None:
if ylims[0] is not None:
def_ylims[0] = min([def_ylims[0], ylims[0]])
if ylims[1] is not None:
def_ylims[1] = max([def_ylims[1], ylims[1]])
ax_ts.set_ylim(def_ylims)
yticks = sorted(def_ylims)
ax_ts.set_yticks(yticks)
ax_ts.set_yticklabels(['%.02f' % y for y in yticks])
yrange = def_ylims[1] - def_ylims[0]
# Plot average
if cutoff is None:
cutoff = []
cutoff.insert(0, tseries[~np.isnan(tseries)].mean())
for i, thr in enumerate(cutoff):
ax_ts.plot((0, ntsteps - 1), [thr] * 2,
linewidth=.75,
linestyle='-' if i == 0 else ':',
color=color if i == 0 else 'k')
if i == 0:
mean_label = r'$\mu$=%.3f%s' % (thr, units if units is not None else '')
ax_ts.annotate(
mean_label, xy=(ntsteps - 1, thr), xytext=(11, 0),
textcoords='offset points', va='center', color='w', size=10,
bbox=dict(boxstyle='round', fc=color, ec='none', color='none', lw=0),
arrowprops=dict(
arrowstyle='wedge,tail_width=0.8', lw=0, patchA=None, patchB=None,
fc=color, ec='none', relpos=(0.01, 0.5)))
else:
y_off = [0.0, 0.0]
for pth in cutoff[:i]:
inc = abs(thr - pth)
if inc < yrange:
factor = (- (inc / yrange) + 1) ** 2
if (thr - pth) < 0.0:
y_off[0] -= factor * 20
else:
y_off[1] += factor * 20
offset = y_off[0] if abs(y_off[0]) > y_off[1] else y_off[1]
a_label = '%.2f%s' % (thr, units if units is not None else '')
ax_ts.annotate(
a_label, xy=(ntsteps - 1, thr), xytext=(11, offset),
textcoords='offset points', va='center',
color='w', size=10,
bbox=dict(boxstyle='round', fc='dimgray', ec='none', color='none', lw=0),
arrowprops=dict(
arrowstyle='wedge,tail_width=.9', lw=0, patchA=None, patchB=None,
fc='dimgray', ec='none', relpos=(.1, .5)))
if not gs_dist is None:
ax_dist = plt.subplot(gs_dist)
sns.displot(tseries, vertical=True, ax=ax_dist)
ax_dist.set_xlabel('Timesteps')
ax_dist.set_ylim(ax_ts.get_ylim())
ax_dist.set_yticklabels([])
return [ax_ts, ax_dist], gs
else:
return ax_ts, gs
def confoundplot(tseries, gs_ts, gs_dist=None, name=None, normalize=True,
units=None, tr=None, hide_x=True, color='b', nskip=4):
# Define TR and number of frames
notr = False
if tr is None:
notr = True
tr = 1.
ntsteps = len(tseries)
# Normalize time series
tseries = np.array(tseries)
if normalize:
tseries /= tr
# Define nested GridSpec
gs = mgs.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_ts,
width_ratios=[1, 100], wspace=0.0)
ax_ts = plt.subplot(gs[1])
ax_ts.grid(False)
ax_ts.plot(tseries, color=color)
ax_ts.set_xlim((0, ntsteps - 1))
# Set 10 frame markers in X axis
interval = ntsteps // 10
xticks = list(range(0, ntsteps)[::interval]) + [ntsteps - 1]
ax_ts.set_xticks(xticks)
if not hide_x:
if notr:
ax_ts.set_xlabel('time (frame #)')
else:
ax_ts.set_xlabel('time (s)')
labels = tr * np.array(xticks)
ax_ts.set_xticklabels(['%.02f' % t for t in labels.tolist()])
else:
ax_ts.set_xticklabels([])
if not name is None:
var_label = name
if not units is None:
var_label += (' [{}]' if notr else ' [{}/s]').format(units)
ax_ts.set_ylabel(var_label)
for side in ["top", "right"]:
ax_ts.spines[side].set_color('none')
ax_ts.spines[side].set_visible(False)
if not hide_x:
ax_ts.spines["bottom"].set_position(('outward', 20))
ax_ts.xaxis.set_ticks_position('bottom')
else:
ax_ts.spines["bottom"].set_color('none')
ax_ts.spines["bottom"].set_visible(False)
ax_ts.spines["left"].set_position(('outward', 30))
ax_ts.yaxis.set_ticks_position('left')
# Plot average
ax_ts.plot((0, ntsteps), [tseries.mean()] * 2, color=color, linestyle=':')
ax_ts.set_ylim(tseries[nskip:].min(), tseries[nskip:].max())
if not gs_dist is None:
ax_dist = plt.subplot(gs_dist)
sns.displot(tseries, vertical=True, ax=ax_dist)
ax_dist.set_xlabel('Timesteps')
ax_dist.set_ylim(ax_ts.get_ylim())
ax_dist.set_yticklabels([])
return [ax_ts, ax_dist], gs
else:
return ax_ts, gs
def confoundplot(tseries, gs_ts, gs_dist=None, name=None,
units=None, tr=None, hide_x=True, color='b', nskip=0,
cutoff=None, ylims=None):
# Define TR and number of frames
notr = False
if tr is None:
notr = True
tr = 1.
ntsteps = len(tseries)
tseries = np.array(tseries)
# Define nested GridSpec
gs = mgs.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_ts,
width_ratios=[1, 100], wspace=0.0)
ax_ts = plt.subplot(gs[1])
ax_ts.grid(False)
# Set 10 frame markers in X axis
interval = max((ntsteps // 10, ntsteps // 5, 1))
xticks = list(range(0, ntsteps)[::interval])
ax_ts.set_xticks(xticks)
if not hide_x:
if notr:
ax_ts.set_xlabel('time (frame #)')
else:
ax_ts.set_xlabel('time (s)')
labels = tr * np.array(xticks)
ax_ts.set_xticklabels(['%.02f' % t for t in labels.tolist()])
else:
ax_ts.set_xticklabels([])
if name is not None:
if units is not None:
name += ' [%s]' % units
ax_ts.annotate(
name, xy=(0.0, 0.7), xytext=(0, 0), xycoords='axes fraction',
textcoords='offset points', va='center', ha='left',
color=color, size=8,
bbox={'boxstyle': 'round', 'fc': 'w', 'ec': 'none',
'color': 'none', 'lw': 0, 'alpha': 0.8})
for side in ["top", "right"]:
ax_ts.spines[side].set_color('none')
ax_ts.spines[side].set_visible(False)
if not hide_x:
ax_ts.spines["bottom"].set_position(('outward', 20))
ax_ts.xaxis.set_ticks_position('bottom')
else:
ax_ts.spines["bottom"].set_color('none')
ax_ts.spines["bottom"].set_visible(False)
# ax_ts.spines["left"].set_position(('outward', 30))
ax_ts.spines["left"].set_color('none')
ax_ts.spines["left"].set_visible(False)
# ax_ts.yaxis.set_ticks_position('left')
ax_ts.set_yticks([])
ax_ts.set_yticklabels([])
nonnan = tseries[~np.isnan(tseries)]
if nonnan.size > 0:
# Calculate Y limits
valrange = (nonnan.max() - nonnan.min())
def_ylims = [nonnan.min() - 0.1 * valrange, nonnan.max() + 0.1 * valrange]
if ylims is not None:
if ylims[0] is not None:
def_ylims[0] = min([def_ylims[0], ylims[0]])
if ylims[1] is not None:
def_ylims[1] = max([def_ylims[1], ylims[1]])
# Add space for plot title and mean/SD annotation
def_ylims[0] -= 0.1 * (def_ylims[1] - def_ylims[0])
ax_ts.set_ylim(def_ylims)
# Annotate stats
maxv = nonnan.max()
mean = nonnan.mean()
stdv = nonnan.std()
p95 = np.percentile(nonnan, 95.0)
else:
maxv = 0
mean = 0
stdv = 0
p95 = 0
stats_label = (r'max: {max:.3f}{units} $\bullet$ mean: {mean:.3f}{units} '
r'$\bullet$ $\sigma$: {sigma:.3f}').format(
max=maxv, mean=mean, units=units or '', sigma=stdv)
ax_ts.annotate(
stats_label, xy=(0.98, 0.7), xycoords='axes fraction',
xytext=(0, 0), textcoords='offset points',
va='center', ha='right', color=color, size=4,
bbox={'boxstyle': 'round', 'fc': 'w', 'ec': 'none', 'color': 'none',
'lw': 0, 'alpha': 0.8}
)
# Annotate percentile 95
ax_ts.plot((0, ntsteps - 1), [p95] * 2, linewidth=.1, color='lightgray')
ax_ts.annotate(
'%.2f' % p95, xy=(0, p95), xytext=(-1, 0),
textcoords='offset points', va='center', ha='right',
color='lightgray', size=3)
if cutoff is None:
cutoff = []
for i, thr in enumerate(cutoff):
ax_ts.plot((0, ntsteps - 1), [thr] * 2,
linewidth=.2, color='dimgray')
ax_ts.annotate(
'%.2f' % thr, xy=(0, thr), xytext=(-1, 0),
textcoords='offset points', va='center', ha='right',
color='dimgray', size=3)
ax_ts.plot(tseries, color=color, linewidth=.8)
ax_ts.set_xlim((0, ntsteps - 1))
if gs_dist is not None:
ax_dist = plt.subplot(gs_dist)
sns.displot(tseries, vertical=True, ax=ax_dist)
ax_dist.set_xlabel('Timesteps')
ax_dist.set_ylim(ax_ts.get_ylim())
ax_dist.set_yticklabels([])
return [ax_ts, ax_dist], gs
return ax_ts, gs