def corrplot(data,
names=None,
sig_stars=True,
sig_tail="both",
sig_corr=True,
cmap="Spectral_r",
cmap_range=None,
cbar=True,
ax=None,
**kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : nvars x nobs array
data array where rows are variables and columns are observations
names : sequence of strings
names to associate with variables; should be short
sig_stars : bool
if True, get significance with permutation test and denote with stars
sig_tail : both | upper | lower
direction for significance test
sig_corr : bool
if True, use FWE-corrected significance
cmap : colormap
colormap name as string or colormap object
cmap_range : None, "full", (low, high)
either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap
cbar : boolean
if true, plots the colorbar legend
kwargs : other keyword arguments
passed to ax.matshow()
Returns
-------
ax : matplotlib axis
axis object with plot
"""
corrmat = np.corrcoef(data)
if sig_stars:
p_mat = moss.randomize_corrmat(data, sig_tail, sig_corr)
else:
p_mat = None
if cmap_range is None:
triu = np.triu_indices(len(data), 1)
vmax = min(1, np.max(np.abs(corrmat[triu])) * 1.15)
vmin = -vmax
cmap_range = vmin, vmax
elif cmap_range == "full":
cmap_range = (-1, 1)
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range, cbar, ax,
**kwargs)
return ax
def corrplot(data, names=None, sig_stars=True, sig_tail="both", sig_corr=True,
cmap="Spectral_r", cmap_range=None, cbar=True, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : nvars x nobs array
data array where rows are variables and columns are observations
names : sequence of strings
names to associate with variables; should be short
sig_stars : bool
if True, get significance with permutation test and denote with stars
sig_tail : both | upper | lower
direction for significance test
sig_corr : bool
if True, use FWE-corrected significance
cmap : colormap
colormap name as string or colormap object
cmap_range : None, "full", (low, high)
either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap
cbar : boolean
if true, plots the colorbar legend
kwargs : other keyword arguments
passed to ax.matshow()
Returns
-------
ax : matplotlib axis
axis object with plot
"""
corrmat = np.corrcoef(data)
if sig_stars:
p_mat = moss.randomize_corrmat(data, sig_tail, sig_corr)
else:
p_mat = None
if cmap_range is None:
triu = np.triu_indices(len(data), 1)
vmax = min(1, np.max(np.abs(corrmat[triu])) * 1.15)
vmin = -vmax
cmap_range = vmin, vmax
elif cmap_range == "full":
cmap_range = (-1, 1)
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, ax, **kwargs)
return ax
def corrplot(data, names=None, annot=True, sig_stars=True, sig_tail="both",
sig_corr=True, cmap=None, cmap_range=None, cbar=True,
diag_names=True, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : Dataframe or nobs x nvars array
Rectangular nput data with variabes in the columns.
names : sequence of strings
Names to associate with variables if `data` is not a DataFrame.
annot : bool
Whether to annotate the upper triangle with correlation coefficients.
sig_stars : bool
If True, get significance with permutation test and denote with stars.
sig_tail : both | upper | lower
Direction for significance test. Also controls the default colorbar.
sig_corr : bool
If True, use FWE-corrected p values for the sig stars.
cmap : colormap
Colormap name as string or colormap object.
cmap_range : None, "full", (low, high)
Either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap.
cbar : bool
If true, plot the colorbar legend.
ax : matplotlib axis
Axis to draw plot in.
kwargs : other keyword arguments
Passed to ax.matshow()
Returns
-------
ax : matplotlib axis
Axis object with plot.
"""
if not isinstance(data, pd.DataFrame):
if names is None:
names = ["var_%d" % i for i in range(data.shape[1])]
data = pd.DataFrame(data, columns=names, dtype=np.float)
# Calculate the correlation matrix of the dataframe
corrmat = data.corr()
# Pandas will drop non-numeric columns; let's keep track of that operation
names = corrmat.columns
data = data[names]
# Get p values with a permutation test
if annot and sig_stars:
p_mat = moss.randomize_corrmat(data.values.T, sig_tail, sig_corr)
else:
p_mat = None
# Sort out the color range
if cmap_range is None:
triu = np.triu_indices(len(corrmat), 1)
vmax = min(1, np.max(np.abs(corrmat.values[triu])) * 1.15)
vmin = -vmax
if sig_tail == "both":
cmap_range = vmin, vmax
elif sig_tail == "upper":
cmap_range = 0, vmax
elif sig_tail == "lower":
cmap_range = vmin, 0
elif cmap_range == "full":
cmap_range = (-1, 1)
# Find a colormapping, somewhat intelligently
if cmap is None:
if min(cmap_range) >= 0:
cmap = "PuRd"
elif max(cmap_range) <= 0:
cmap = "GnBu_r"
else:
cmap = "coolwarm"
if cmap == "jet":
# Paternalism
raise ValueError("Never use the 'jet' colormap!")
# Plot using the more general symmatplot function
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, annot, diag_names, ax, **kwargs)
return ax
def corrplot(data, names=None, annot=True, sig_stars=True, sig_tail="both",
sig_corr=True, cmap=None, cmap_range=None, cbar=True,
diag_names=True, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : Dataframe or nobs x nvars array
Rectangular nput data with variabes in the columns.
names : sequence of strings
Names to associate with variables if `data` is not a DataFrame.
annot : bool
Whether to annotate the upper triangle with correlation coefficients.
sig_stars : bool
If True, get significance with permutation test and denote with stars.
sig_tail : both | upper | lower
Direction for significance test. Also controls the default colorbar.
sig_corr : bool
If True, use FWE-corrected p values for the sig stars.
cmap : colormap
Colormap name as string or colormap object.
cmap_range : None, "full", (low, high)
Either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap.
cbar : bool
If true, plot the colorbar legend.
ax : matplotlib axis
Axis to draw plot in.
kwargs : other keyword arguments
Passed to ax.matshow()
Returns
-------
ax : matplotlib axis
Axis object with plot.
"""
if not isinstance(data, pd.DataFrame):
if names is None:
names = ["var_%d" % i for i in range(data.shape[1])]
data = pd.DataFrame(data, columns=names, dtype=np.float)
# Calculate the correlation matrix of the dataframe
corrmat = data.corr()
# Pandas will drop non-numeric columns; let's keep track of that operation
names = corrmat.columns
data = data[names]
# Get p values with a permutation test
if annot and sig_stars:
p_mat = moss.randomize_corrmat(data.values.T, sig_tail, sig_corr)
else:
p_mat = None
# Sort out the color range
if cmap_range is None:
triu = np.triu_indices(len(corrmat), 1)
vmax = min(1, np.max(np.abs(corrmat.values[triu])) * 1.15)
vmin = -vmax
if sig_tail == "both":
cmap_range = vmin, vmax
elif sig_tail == "upper":
cmap_range = 0, vmax
elif sig_tail == "lower":
cmap_range = vmin, 0
elif cmap_range == "full":
cmap_range = (-1, 1)
# Find a colormapping, somewhat intelligently
if cmap is None:
if min(cmap_range) >= 0:
cmap = "OrRd"
elif max(cmap_range) <= 0:
cmap = "PuBu_r"
else:
cmap = "coolwarm"
if cmap == "jet":
# Paternalism
raise ValueError("Never use the 'jet' colormap!")
# Plot using the more general symmatplot function
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, annot, diag_names, ax, **kwargs)
return ax
def corrplot(data, names=None, sig_stars=True, sig_tail="both", sig_corr=True,
cmap="coolwarm", cmap_range=None, cbar=True, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : Dataframe or nobs x nvars array
input data
names : sequence of strings
names to associate with variables; should be short
sig_stars : bool
if True, get significance with permutation test and denote with stars
sig_tail : both | upper | lower
direction for significance test
sig_corr : bool
if True, use FWE-corrected significance
cmap : colormap
colormap name as string or colormap object
cmap_range : None, "full", (low, high)
either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap
cbar : boolean
if true, plots the colorbar legend
kwargs : other keyword arguments
passed to ax.matshow()
Returns
-------
ax : matplotlib axis
axis object with plot
"""
if not isinstance(data, pd.DataFrame):
if names is None:
names = ["var_%d" % i for i in range(data.shape[1])]
data = pd.DataFrame(data, columns=names, dtype=np.float)
# Calculate the correlation matrix of the dataframe
corrmat = data.corr()
# Pandas will drop non-numeric columns; let's keep track of that operation
names = corrmat.columns
data = data[names]
# Get p values with a permutation test
if sig_stars:
p_mat = moss.randomize_corrmat(data.values.T, sig_tail, sig_corr)
else:
p_mat = None
# Paternalism
if cmap == "jet":
raise ValueError("Never use the 'jet' colormap!")
# Sort out the color range
if cmap_range is None:
triu = np.triu_indices(len(corrmat), 1)
vmax = min(1, np.max(np.abs(corrmat.values[triu])) * 1.15)
vmin = -vmax
cmap_range = vmin, vmax
elif cmap_range == "full":
cmap_range = (-1, 1)
# Plot using the more general symmatplot function
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, ax, **kwargs)
return ax
def corrplot(data, names=None, sig_stars=True, sig_tail="both", sig_corr=True,
cmap="coolwarm", cmap_range=None, cbar=True, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
Parameters
----------
data : Dataframe or nobs x nvars array
input data
names : sequence of strings
names to associate with variables; should be short
sig_stars : bool
if True, get significance with permutation test and denote with stars
sig_tail : both | upper | lower
direction for significance test
sig_corr : bool
if True, use FWE-corrected significance
cmap : colormap
colormap name as string or colormap object
cmap_range : None, "full", (low, high)
either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap
cbar : boolean
if true, plots the colorbar legend
kwargs : other keyword arguments
passed to ax.matshow()
Returns
-------
ax : matplotlib axis
axis object with plot
"""
if not isinstance(data, pd.DataFrame):
if names is None:
names = ["var_%d" % i for i in range(data.shape[1])]
data = pd.DataFrame(data, columns=names, dtype=np.float)
# Calculate the correlation matrix of the dataframe
corrmat = data.corr()
# Pandas will drop non-numeric columns; let's keep track of that operation
names = corrmat.columns
data = data[names]
# Get p values with a permutation test
if sig_stars:
p_mat = moss.randomize_corrmat(data.values.T, sig_tail, sig_corr)
else:
p_mat = None
# Paternalism
if cmap == "jet":
raise ValueError("Never use the 'jet' colormap!")
# Sort out the color range
if cmap_range is None:
triu = np.triu_indices(len(corrmat), 1)
vmax = min(1, np.max(np.abs(corrmat.values[triu])) * 1.15)
vmin = -vmax
cmap_range = vmin, vmax
elif cmap_range == "full":
cmap_range = (-1, 1)
# Plot using the more general symmatplot function
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, ax, **kwargs)
return ax
