def profile_p(p, x, ax=None, **kwargs):
"""Plot atmospheric profile against pressure in linear space.
Parameters:
p (ndarray): Pressure [Pa].
x (ndarray): Atmospheric property.
ax (AxesSubplot): Axes to plot in.
**kwargs: Additional keyword arguments passed to `plt.plot`.
See also:
:func:`~typhon.plots.profile_p_log`
Plot profile against pressure in log space.
:func:`~typhon.plots.profile_z`
Plot profile against height.
Examples:
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
import typhon.plots
p = typhon.math.nlogspace(1000e2, 0.1e2, 50) # pressure in Pa.
x = np.exp(p / p[0])
fig, ax = plt.subplots()
typhon.plots.profile_p(p, x, ax=ax)
plt.show()
"""
if ax is None:
ax = plt.gca()
# Label and format for yaxis.
formatter.set_yaxis_formatter(formatter.HectoPascalFormatter(), ax=ax)
if ax.get_subplotspec().is_first_col():
ax.set_ylabel('Pressure [hPa]')
# Actual plot.
ret = ax.plot(x, p, **kwargs)
if hasattr(ax.yaxis, 'set_inverted'):
# Matplotlib >=3.1.0
# In matplotlib==3.1.0 the yaxis has to be inverted after actual plot
# (https://github.com/matplotlib/matplotlib/issues/14615)
ax.yaxis.set_inverted(True)
elif not ax.yaxis_inverted():
# Matplotlib <=3.0.3
ax.invert_yaxis()
return ret
def profile_z(z, x, ax=None, **kwargs):
"""Plot atmospheric profile of arbitrary property against height (in km).
Parameters:
z (ndarray): Height [m].
x (ndarray): Atmospheric property.
ax (AxesSubplot): Axes to plot in.
**kwargs: Additional keyword arguments passed to `plt.plot`.
See also:
:func:`~typhon.plots.profile_p`
Plot profile against pressure in linear space.
:func:`~typhon.plots.profile_p_log`
Plot profile against pressure in log space.
Examples:
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
import typhon.plots
z = np.linspace(0, 80e3, 50)
x = np.sin(z / 5e3)
fig, ax = plt.subplots()
typhon.plots.profile_z(z, x, ax=ax)
plt.show()
"""
if ax is None:
ax = plt.gca()
# Determine min/max pressure of data in plot. The current axis limits are
# also taken into account. This ensures complete data coverage when using
# the function iteratively on the same axis.
zmin = np.min((np.min(z), *ax.get_ylim()))
zmax = np.max((np.max(z), *ax.get_ylim()))
ax.set_ylim(zmin, zmax)
# Label and format for yaxis.
if ax.is_first_col():
ax.set_ylabel('Height [km]')
# Actual plot.
ret = ax.plot(x, z, **kwargs)
formatter.set_yaxis_formatter(formatter.ScalingFormatter('kilo', '{x:g}'),
ax=ax)
return ret
def profile_p(p, x, ax=None, **kwargs):
"""Plot atmospheric profile against pressure in linear space.
Parameters:
p (ndarray): Pressure [Pa].
x (ndarray): Atmospheric property.
ax (AxesSubplot): Axes to plot in.
**kwargs: Additional keyword arguments passed to `plt.plot`.
See also:
:func:`~typhon.plots.profile_p_log`
Plot profile against pressure in log space.
:func:`~typhon.plots.profile_z`
Plot profile against height.
Examples:
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
import typhon.plots
p = typhon.math.nlogspace(1000e2, 0.1e2, 50) # pressure in Pa.
x = np.exp(p / p[0])
fig, ax = plt.subplots()
typhon.plots.profile_p(p, x, ax=ax)
plt.show()
"""
if ax is None:
ax = plt.gca()
# Determine min/max pressure of data in plot. The current axis limits are
# also taken into account. This ensures complete data coverage when using
# the function iteratively on the same axis.
pmin = np.min((np.min(p), *ax.get_ylim()))
pmax = np.max((np.max(p), *ax.get_ylim()))
ax.set_ylim(pmax, pmin) # implicitly invert yaxis
# Label and format for yaxis.
formatter.set_yaxis_formatter(formatter.HectoPascalFormatter(), ax=ax)
if ax.is_first_col():
ax.set_ylabel('Pressure [hPa]')
# Actual plot.
return ax.plot(x, p, **kwargs)
def profile_p_log(p, x, ax=None, **kwargs):
"""Plot atmospheric profile against pressure in log space.
This function is a wrapper for :func:`~typhon.plots.profile_p`:
The input values as well as additional keyword arguments are passed through
and the yscale is set to "log".
Parameters:
p (ndarray): Pressure [Pa].
x (ndarray): Atmospheric property.
ax (AxesSubplot): Axes to plot in.
**kwargs: Additional keyword arguments passed to `plt.plot`.
See also:
:func:`~typhon.plots.profile_p`
Plot profile against pressure in linear space.
:func:`~typhon.plots.profile_z`
Plot profile against height.
Examples:
.. plot::
:include-source:
import numpy as np
import matplotlib.pyplot as plt
import typhon.plots
p = typhon.math.nlogspace(1000e2, 0.1e2, 50)
x = np.exp(p / p[0])
fig, ax = plt.subplots()
typhon.plots.profile_p_log(p, x, ax=ax)
plt.show()
"""
if ax is None:
ax = plt.gca()
# Call ``set_yscale`` before plot to prevent reset of axis inverting in
# matplotlib 3.1.0 (https://github.com/matplotlib/matplotlib/issues/14620)
ax.set_yscale('log')
ret = profile_p(p, x, ax=ax, **kwargs)
# Set logarithmic scale.
formatter.set_yaxis_formatter(formatter.HectoPascalLogFormatter(), ax=ax)
return ret
def plot_from_konrad_diff(variable,exps,exp_ref,
same=False,
delta=10,
delta_ref=5,
xrefline=0,
xrefline_ref=None,
title=None,
labels=None,
styles=None,
label_ref=None,
style_ref=None
):
"""Plots variable profiles from several `konrad` experiments in relation to a
given reference experiment.
Parameters
----------
variable : str
Variable name in the output file.
exps : list
List of strings describing the paths to the output files.
exp_ref : str
String describing the path to the output file for the
reference experiment.
same : bool, optional
If `True`, profiles are plotted in the same pair of axes.
Otherwise, there are as many pairs of axes as profiles
(`False` is the default). Reference profile has always its own
pair of axes.
delta : scalar, optional
Offset added to the extrema of the data.
delta_ref : scalar, optional
Offset added to the extrema of the reference data.
xrefline : scalar, optional
If `same` is `True`, plots a vertical reference line at the
given scalar value. If `same` is `False`, plots vertical
reference lines at each axis. By default the lines are zero
lines.
xrefline_ref : scalar or `None` optional
Plots a vertical reference line at the given scalar value. If
`None`, no reference line is plotted: this is the default.
title : str or `None`, optional
An overarching title for the figure (`None` is the default).
labels, styles : dict or `None`, optional
Dictionaries containing profile labels and line styles.
Usually created by zipping two lists, e.g.
`labels = dict(zip(exps,labels))`. The `styles` dictionaries
are passed as keyword arguments to the `plt.plot` function. If
`None`, one gets default styles and/or labels.
label_ref : str or `None`, optional
String containing reference profile label.
style_ref : dict or `None`, optional
Dictionary containing reference profile line styles.
Returns
-------
fig : matplotlib.Figure
Figure with the plots.
axes : matplotlib.Axes
Axes for the figure.
"""
(p,
data,minimum_diff,maximum_diff,
minimum_ref,maximum_ref
) = get_from_konrad_ref(variable,exps,exp_ref) # Gets the data
n = len(list(data.keys())) # Experiment count
ext_exps = list(data.keys()) # Experiment list
width = 2 # Width (two pairs)
height = 5 # Height
if same:
fig = plt.figure(dpi=300,figsize=(width*2,height))
axes = {
"ref" : fig.add_subplot(1,2,1),
"diff" : fig.add_subplot(1,2,2)
}
for e in data: # Plotting
ax = axes["diff"]
labelsd = labels
stylesd = styles
if e == exp_ref:
ax = axes["ref"]
labelsd = label_ref
stylesd = style_ref
if isinstance(label_ref,str):
labelsd = { e : label_ref }
if isinstance(style_ref,dict):
stylesd = { e : style_ref }
if labelsd == None:
if stylesd == None:
ax.plot(data[e],p)
elif stylesd[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**stylesd[e])
elif labelsd[e] == None:
if stylesd == None:
ax.plot(data[e],p)
elif stylesd[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**stylesd[e])
else:
if stylesd == None:
ax.plot(data[e],p,label=labelsd[e])
elif stylesd[e] == None:
ax.plot(data[e],p,label=labelsd[e])
else:
labels_and_styles=stylesd[e]
labels_and_styles["label"]=labelsd[e]
ax.plot(data[e],p,**labels_and_styles)
for e in axes:
ax=axes[e]
ax.invert_yaxis() # Settings for y-axes
ax.set_yscale("log")
if e != "ref":
ax.tick_params(axis='y',which='both',labelleft=False)
if e == "ref":
formatter.set_yaxis_formatter(formatter.HectoPascalLogFormatter(),
ax=ax)
ax.tick_params(axis='y',which='both',labelsize=6)
ax.set_ylabel(p_a,fontsize=6)
if e != "ref": # Settings for x-axes
ax.set_xlim(minimum_diff - delta,maximum_diff + delta)
ax.set_xlabel(Dvariable_label[variable],fontsize=6)
if e == "ref":
ax.set_xlim(minimum_ref - delta_ref,maximum_ref + delta_ref)
ax.set_xlabel(variable_label[variable],fontsize=6)
ax.tick_params(axis='x',which='both',labeltop=False,labelsize=6)
ax.minorticks_off() # Unsets minor ticks
if e != "ref": # Reference line
ax.axvline(x=xrefline,lw=0.7,color=(0,0,0))
if ( e == "ref" and xrefline_ref != None ):
ax.axvline(x=xrefline_ref,lw=0.7,color=(0,0,0))
if e != "ref":
ax.legend(fontsize=5) # Legend
if ( e == "ref" and label_ref != None ):
ax.set_title(label_ref,fontsize=5)
else:
fig = plt.figure(dpi=300,figsize=(width*n,height))
axes = { ext_exps[i-1] : fig.add_subplot(1,n,i) for i in range(1,n+1) }
for e in axes: # Plotting
ax = axes[e]
labelsd = labels
stylesd = styles
if e == exp_ref:
labelsd = label_ref
stylesd = style_ref
if isinstance(label_ref,str):
labelsd = { e : label_ref }
if isinstance(style_ref,dict):
stylesd = { e : style_ref }
if labelsd == None:
if stylesd == None:
ax.plot(data[e],p)
elif stylesd[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**stylesd[e])
elif labelsd[e] == None:
if stylesd == None:
ax.plot(data[e],p)
elif stylesd[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**stylesd[e])
else:
if stylesd == None:
ax.plot(data[e],p,label=labelsd[e])
elif stylesd[e] == None:
ax.plot(data[e],p,label=labelsd[e])
else:
labels_and_styles=stylesd[e]
labels_and_styles["label"]=labelsd[e]
ax.plot(data[e],p,**labels_and_styles)
ax.invert_yaxis() # Settings for y-axes
ax.set_yscale("log")
if e != exp_ref:
ax.tick_params(axis='y',which='both',labelleft=False)
if e == exp_ref:
formatter.set_yaxis_formatter(formatter.HectoPascalLogFormatter(),
ax=ax)
ax.tick_params(axis='y',which='both',labelsize=6)
ax.set_ylabel(p_a,fontsize=6)
if e != exp_ref: # Settings for x-axes
ax.set_xlim(minimum_diff - delta,maximum_diff + delta)
if e == exp_ref:
ax.set_xlim(minimum_ref - delta_ref,maximum_ref + delta_ref)
if e != exp_ref:
ax.set_xlabel(Dvariable_label[variable],fontsize=6)
if e == exp_ref:
ax.set_xlabel(variable_label[variable],fontsize=6)
ax.tick_params(axis='x',which='both',labeltop=False,labelsize=6)
ax.minorticks_off() # Unsets minor ticks
if e != exp_ref: # Reference line
ax.axvline(x=xrefline,lw=0.7,color=(0,0,0))
if ( e == exp_ref and xrefline_ref != None ):
ax.axvline(x=xrefline_ref,lw=0.7,color=(0,0,0))
if e != exp_ref: # Set axes titles
ax.set_title(labels[e],fontsize=5)
if ( e == exp_ref and label_ref != None ):
ax.set_title(label_ref,fontsize=5)
if title != None: # Title settings
fig.suptitle(title,fontsize=5,y=0.95)
fig.set_tight_layout("tight") # Other settings
return (fig,axes)
def plot_from_konrad(variable,exps,
same=False,
delta=10,
xrefline=None,
title=None,
labels=None,
styles=None
):
"""Plots variable profiles from several `konrad` experiments.
Parameters
----------
variable : str
Variable name in the output file.
exps : list
List of strings describing the paths to the output files.
same : bool, optional
If `True`, profiles are plotted in the same pair of axes.
Otherwise, there are as many pairs of axes as profiles
(`False` is the default).
delta : scalar, optional
Offset added to the extrema of the data.
xrefline : scalar or `None`, optional
If `same` is `True`, plots a vertical reference line at the
given scalar value. If `same` is `False`, plots vertical
reference lines at each axis. If `None`, plots no vertical
reference lines (`None` is the default).
title : str or `None`, optional
An overarching title for the figure (`None` is the default).
labels, styles : dict or `None`, optional
Dictionaries containing profile labels and line styles.
Usually created by zipping two lists, e.g.
`labels = dict(zip(exps,labels))`. The `styles` dictionaries
are passed as keyword arguments to the `plt.plot` function.
If `None`, one gets default styles and/or labels.
Returns
-------
fig : matplotlib.Figure
Figure with the plots.
axes : matplotlib.Axes
Axes for the figure.
"""
(p,data,minimum,maximum) = get_from_konrad(variable,exps) # Gets the data
n = len(exps) # Experiment count
width = 1 # Width (one pair)
height = 5 # Height
if same:
fig = plt.figure(dpi=300,figsize=(3*width,height))
axes = fig.add_subplot(1,1,1)
for e in data: # Plotting
ax = axes
if labels == None:
if styles == None:
ax.plot(data[e],p)
elif styles[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**styles[e])
elif labels[e] == None:
if styles == None:
ax.plot(data[e],p)
elif styles[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**styles[e])
else:
if styles == None:
ax.plot(data[e],p,label=labels[e])
elif styles[e] == None:
ax.plot(data[e],p,label=labels[e])
else:
labels_and_styles = styles[e]
labels_and_styles["label"] = labels[e]
ax.plot(data[e],p,**labels_and_styles)
ax = axes
ax.invert_yaxis() # Settings for y-axis
ax.set_yscale("log")
formatter.set_yaxis_formatter(formatter.HectoPascalLogFormatter(),
ax=ax)
ax.tick_params(axis='y',which='both',labelsize=6)
ax.set_ylabel(p_a,fontsize=6)
ax.set_xlim(minimum - delta,maximum + delta) # Settings for x-axis
ax.tick_params(axis='x',which='both',labeltop=False,labelsize=6)
ax.set_xlabel(variable_label[variable],fontsize=6)
ax.minorticks_off() # Unsets minor ticks
if xrefline != None: # Reference line
ax.axvline(x=xrefline,lw=0.7,color=(0,0,0))
ax.legend(fontsize=5) # Legend
else:
fig = plt.figure(dpi=300,figsize=(n*width,height))
axes = { exps[i-1] : fig.add_subplot(1,n,i) for i in range(1,n+1) }
for e in axes: # Plotting
ax = axes[e]
if labels == None:
if styles == None:
ax.plot(data[e],p)
elif styles[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**styles[e])
elif labels[e] == None:
if styles == None:
ax.plot(data[e],p)
elif styles[e] == None:
ax.plot(data[e],p)
else:
ax.plot(data[e],p,**styles[e])
else:
if styles == None:
ax.plot(data[e],p,label=labels[e])
elif styles[e] == None:
ax.plot(data[e],p,label=labels[e])
else:
labels_and_styles = styles[e]
labels_and_styles["label"] = labels[e]
ax.plot(data[e],p,**labels_and_styles)
ax.invert_yaxis() # Settings for y-axes
ax.set_yscale("log")
if e != exps[0]:
ax.tick_params(axis='y',which='both',labelleft=False)
if e == exps[0]:
formatter.set_yaxis_formatter(formatter.HectoPascalLogFormatter(),
ax=ax)
ax.tick_params(axis='y',which='both',labelsize=6)
ax.set_ylabel(p_a,fontsize=6)
ax.set_xlim(minimum - delta,maximum + delta) # Settings for x-axes
ax.tick_params(axis='x',which='both',labeltop=False,labelsize=6)
ax.set_xlabel(variable_label[variable],fontsize=6)
ax.minorticks_off() # Unsets minor ticks
if xrefline != None: # Reference line
ax.axvline(x=xrefline,lw=0.7,color=(0,0,0))
ax.set_title(labels[e],fontsize=5) # Sets axes titles
if title != None: # Title settings
fig.suptitle(title,fontsize=5,y=0.95)
fig.set_tight_layout("tight") # Other settings
return (fig,axes)