applied suggesteds changhes

src/icesat2_tracks/local_modules/JONSWAP_gamma.py

@@ -1,15 +1,8 @@
-import os
-from scipy.constants import g
-
-if __name__ == "__main__":
-    exec(open(os.environ["PYTHONSTARTUP"]).read())
-    exec(open(STARTUP_2019_DP).read())
 
-    import sys, imp
-
-    from lmfit import minimize, Parameters
-    import copy
+from scipy.constants import g
 
+from lmfit import minimize, Parameters
+import copy
 import matplotlib.pyplot as plt
 import numpy as np
 

src/icesat2_tracks/local_modules/m_general_ph3.py

@@ -124,7 +124,7 @@ def label(self, x="x", y="y", t=None):
     def save(self, name=None, path=None, verbose=True):
         savepath = (
             path
-            if path is not None
+            if path
             else os.path.join(os.path.dirname(os.path.realpath("__file__")), "plot/")
         )
         if not os.path.exists(savepath):
@@ -140,7 +140,7 @@ def save_pup(self, name=None, path=None, verbose=True):
         name = re.sub("\.", "_", name)
         savepath = (
             path
-            if path is not None
+            if path
             else os.path.join(os.path.dirname(os.path.realpath("__file__")), "plot/")
         )
         if not os.path.exists(savepath):
@@ -215,12 +215,7 @@ def power_linear(self, Color="b", fax="f"):
 
         plt.ylabel(("Power db(" + self.data_unit + "^2/" + self.sample_unit + ")"))
         plt.xlabel(xlabelstr)
-        # plt.set_xlabel(fax)
-        # plt.xlim(np.log(fax[1]) ,np.log(fax[-1]))
         plt.xlim(xax[1], xax[-1])
-        # self.F.ax.set_xscale('log')
-        # self.F.ax.semilogx()
-        # ax1.set_xticks([20, 300, 500])
         self.F.make_clear()
         plt.grid()
 
@@ -267,22 +262,22 @@ def __init__(
         fs,
         data,
         clevs=None,
-        sample_unit=None,
-        data_unit=None,
+        sample_unit="df",
+        data_unit="X",
         ylim=None,
-        time_unit=None,
-        cmap=None,
+        time_unit="dt",
+        cmap=plt.cm.ocean_r,
     ):
         self.fs = fs[1:]
         self.time = time
         self.data = data[:, 1:]
         self.clevs = clevs
-        self.sample_unit = sample_unit if sample_unit is not None else "df"
+        self.sample_unit = sample_unit
 
-        self.data_unit = data_unit if data_unit is not None else "X"
-        self.time_unit = time_unit if time_unit is not None else "dt"
+        self.data_unit = data_unit
+        self.time_unit = time_unit
 
-        self.cmap = cmap if cmap is not None else plt.cm.ocean_r
+        self.cmap = cmap
         self.ylim = ylim if ylim is not None else [fs[0], fs[-1]]
 
     def loglog(self):
@@ -340,7 +335,7 @@ def power(self, anomalie=False):
         self.F = FigureAxisXY(10, 4, fig_scale=2)
         dd = 10 * np.log10(self.data[:-1, :])
 
-        if anomalie is True:
+        if anomalie:
             dd_tmp = dd.mean(axis=0).repeat(self.time.size - 1)
             dd = dd - dd_tmp.reshape(self.fs.size, self.time.size - 1).T
             dd = dd
@@ -423,8 +418,8 @@ def power_imshow(
         cbar=True,
     ):
 
-        shading = "gouraud" if shading is True else "flat"
-        fig_size = [10, 4] if fig_size is None else fig_size
+        shading = "gouraud" if shading else "flat"
+        fig_size = [10, 4] if fig_size else fig_size
         if ax:
             assert type(ax) is tuple, "put ax as tuple ax=(ax,F)"
             self.F = ax[1]
@@ -433,12 +428,12 @@ def power_imshow(
             self.F = FigureAxisXY(fig_size[0], fig_size[1], fig_scale=2)
             ax_local = self.F.ax
 
-        if nopower is True:
+        if nopower:
             dd = self.data
         else:
             dd = 10 * np.log10(self.data[:-1, :])
 
-        if anomalie is True:
+        if anomalie:
             dd_tmp = dd.mean(axis=0).repeat(self.time.size - 1)
             dd = dd - dd_tmp.reshape(self.fs.size, self.time.size - 1).T
 
@@ -474,13 +469,13 @@ def power_imshow(
                 dd2 = ddn
                 fn = self.fs[fsn_p]
 
-                if nopower is True:
+                if nopower:
                     tt = tt
                 else:
                     tt = tt[:-1]
 
         else:
-            if nopower is True:
+            if nopower:
                 tt = tt
             else:
                 tt = tt[:-1]
@@ -503,7 +498,7 @@ def power_imshow(
             ttt, fn, dd2, cmap=self.cmap, norm=norm, shading=shading
         )
         plt.ylabel(("f  (" + self.sample_unit + ")"))
-        if cbar is True:
+        if cbar:
             self.cbar = plt.colorbar(self.cs, pad=0.01)
             self.cbar.ax.aspect = 100
             self.cbar.outline.set_linewidth(0)
@@ -570,8 +565,8 @@ def linear_imshow(
         ax=None,
     ):
 
-        shading = "gouraud" if shading is True else "flat"
-        fig_size = [10, 4] if fig_size is None else fig_size
+        shading = "gouraud" if shading else "flat"
+        fig_size = [10, 4] if fig_size else fig_size
         if ax:
             assert type(ax) is tuple, "put ax as tuple ax=(ax,F)"
             self.F = ax[1]
@@ -580,12 +575,12 @@ def linear_imshow(
             self.F = FigureAxisXY(fig_size[0], fig_size[1], fig_scale=2)
             ax_local = self.F.ax
 
-        if nopower is True:
+        if nopower:
             dd = self.data
         else:
             dd = 10 * np.log10(self.data[:-1, :])
 
-        if anomalie is True:
+        if anomalie:
             dd_tmp = dd.mean(axis=0).repeat(self.time.size - 1)
             dd = dd - dd_tmp.reshape(self.fs.size, self.time.size - 1).T
 
@@ -621,13 +616,13 @@ def linear_imshow(
                 dd2 = ddn
                 fn = self.fs[fsn_p]
 
-                if nopower is True:
+                if nopower:
                     tt = tt
                 else:
                     tt = tt[:-1]
 
         else:
-            if nopower is True:
+            if nopower:
                 tt = tt
             else:
                 tt = tt[:-1]
@@ -697,14 +692,14 @@ def set_xaxis_to_days(self, **kwargs):
 
 class PlotPolarspectra:
     def __init__(
-        self, f, thetas, data, unit=None, data_type="fraction", lims=None, verbose=False
+        self, f, thetas, data, unit="X", data_type="fraction", lims=None, verbose=False
     ):
 
         self.f = f
         self.data = data
         self.thetas = thetas
 
-        self.unit = unit if unit is not None else "X"
+        self.unit = unit
 
         # decided on freq limit
         lims = [self.f.min(), self.f.max()] if lims is None else lims
@@ -815,7 +810,7 @@ def plot_scatter_2d(intersect_chain, slope_chain, xname="intersect", yname="slop
     plt.xlabel(xname)
 
 
-def set_timeaxis_days(ax, int1=1, int2=2, bymonthday=None):
+def set_timeaxis_days(ax, int1=1, int2=2, bymonthday=range(1, 32)):
     # int1 interval of the major (labeld) days
     # int2 intercal of the minor (only ticks) days
 
@@ -868,9 +863,7 @@ def easy_dtstr(a):
         return str(a.astype("timedelta64[Y]"))
 
 
-def clevels(data, dstep=None, symmetric=False):
-    dstep = dstep if dstep is not None else 21
-
+def clevels(data, dstep=21, symmetric=False):
     mmax = np.ceil(np.nanmax(data))
     mmin = np.floor(np.nanmin(data))
 
@@ -909,14 +902,14 @@ def read_cdo(file):
 
 def build_timestamp(time, unit, start, verbose=True):
     timestamp = np.datetime64(start) + time[:].astype("m8[" + unit + "]")
-    if verbose is True:
+    if verbose:
         print(timestamp)
     return timestamp
 
 
 def build_timestamp_v2(time, unit, start, verbose=True):
     timestamp = np.datetime64(start) + time[:].astype("datetime64[s]")
-    if verbose is True:
+    if verbose:
         print(timestamp)
     return timestamp
 
@@ -1146,7 +1139,7 @@ def find_max_ts(
         data = np.copy(data_org)
     spreed = 2 if spreed is None else spreed
 
-    if smooth is True:
+    if smooth:
         data = runningmean(data, spreed)
 
     if threshold is not None and threshold > np.nanmin(data):
@@ -1285,7 +1278,7 @@ def __init__(self, span, dt=None, unit=None):
         self.length = -self.span[0] + self.span[1]
         self.loop_iter = np.arange(0, self.length, 1)
         self.index_iter = np.arange(self.span[0], self.span[1], 1)
-        if dt is not None:
+        if dt:
             self.dt = dt
             self.time_iter = self.index_iter * dt
             time_str = []
@@ -1751,7 +1744,7 @@ def RAMSAC_regression_bootstrap(time, freq, time_lin_arg=None, plot=False, **kwa
     """
 
     # NOTE: This function is not called in production. sklearn.bootstrap was depecrated and
-    # the implementation changed.
+    # the implementation needs to be changed.
     import sklearn.bootstrap as boot  # might not work in python 3
 
     RAMS_slope, RAMS_intercept = simple_RAMSAC_regression_estimator(time, freq)

src/icesat2_tracks/local_modules/m_spectrum_ph3.py

@@ -914,7 +914,6 @@ def GetMem(a1,b1,a2,b2, theta):
     # calculate MEM using 1 deg resolution
 
     a = theta.T
-    #a = np.arange(1,361).T
     a = a*np.pi/180.
     a = np.reshape(a,[1,len(a)])
 

src/icesat2_tracks/local_modules/m_tools_ph3.py

@@ -1,4 +1,3 @@
-
 import os
 import matplotlib.pyplot as plt
 import numpy as np
@@ -25,7 +24,7 @@ def tick_formatter(a, interval=2, rounder=2, expt_flag=True, shift=0):
     fact = 10 ** (O - 1)
     b = np.round(a / fact, rounder + 1) * fact
     ticklabels = [" " for i in range(len(b))]
-    
+
     tt = np.arange(shift, len(b), interval)
 
     for t in tt:
@@ -54,18 +53,15 @@ def num_to_str(flt):
 
 
 def mkdirs_r(path):
-    
+
     if not os.path.exists(path):
         os.makedirs(path)
 
 
 def check_year(inputstr, yearstring):
     a = np.datetime64(inputstr).astype(object).year
     ref = np.datetime64(yearstring).astype(object).year
-    if a == ref:
-        return True
-    else:
-        return False
+    return a == ref
 
 
 def datetime64_to_sec(d):
@@ -89,15 +85,12 @@ def sec_to_dt64(pp):
 
 
 def sec_to_float_plot(pp):
-
 
     return dates.date2num(pp.astype("M8[s]").astype(datetime))
 
 
 def sec_to_float_plot_single(pp):
-    return dates.date2num(
-        np.datetime64(int(pp), "s").astype("M8[s]").astype(datetime)
-    )
+    return dates.date2num(np.datetime64(int(pp), "s").astype("M8[s]").astype(datetime))
 
 
 def fake_2d_data(verbose=True, timeaxis=False):
@@ -150,7 +143,6 @@ def pickle_load(name, path, verbose=True):
 
 
 def json_save(name, path, data, verbose=False, return_name=False):
-
 
     if not os.path.exists(path):
         os.makedirs(path)
@@ -198,14 +190,12 @@ def json_load(name, path, verbose=False):
 
 def h5_load(name, path, verbose=False):
 
-
     full_name = os.path.join(path, name + ".h5")
     data = pd.read_hdf(full_name)
     return data
 
 
 def h5_load_v2(name, path, verbose=False):
-
 
     h5f = h5py.File(path + name + ".h5", "r")
     if verbose:
@@ -256,7 +246,6 @@ def h5_save(name, path, data_dict, verbose=False, mode="w"):
 
 def load_pandas_table_dict(name, save_path):
 
-
     warnings.filterwarnings("ignore", category=PerformanceWarning)
 
     return_dict = dict()
@@ -272,8 +261,6 @@ def save_pandas_table(table_dict, ID, save_path):
     if not os.path.exists(save_path):
         os.makedirs(save_path)
 
-
-
     warnings.filterwarnings("ignore", category=PerformanceWarning)
 
     with HDFStore(save_path + "/" + ID + ".h5") as store:
@@ -282,7 +269,6 @@ def save_pandas_table(table_dict, ID, save_path):
 
 
 def write_log(hist, string, verbose=False, short=True, date=True):
-
 
     if short:
         now = datetime.now().strftime("%Y%m%d")
@@ -305,7 +291,6 @@ def add_line_var(ss, name, var):
 
 
 def write_variables_log(hist, var_list, locals, verbose=False, date=False):
-
 
     now = datetime.now().strftime("%Y%m%d")
 
@@ -336,10 +321,7 @@ def save_log_txt(name, path, hist, verbose=False):
         print("saved at: ", full_name)
 
 
-def load_log_txt(name, path):
-
-
-    hist_file = name
+def load_log_txt(hist_file, path):
     f = []
     for h in glob.glob(os.path.join(path, hist_file)):
         f.append(open(h, "r").read())