Merge pull request #14 from jpvantassel/dev

For v0.5.2

LICENSE.txt

@@ -1,6 +1,6 @@
 This license applies to hvsrpy a Python package for horizontal-to-vertical
 spectral ratio processing.
-Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 
                     GNU GENERAL PUBLIC LICENSE
                        Version 3, 29 June 2007

README.md

@@ -3,7 +3,7 @@
 > Joseph P. Vantassel, The University of Texas at Austin
 
 [![DOI](https://zenodo.org/badge/219310971.svg)](https://zenodo.org/badge/latestdoi/219310971)
-[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/jpvantassel/hvsrpy/blob/master/LICENSE.txt)
+[![PyPI - License](https://img.shields.io/pypi/l/hvsrpy)](https://github.com/jpvantassel/hvsrpy/blob/master/LICENSE.txt)
 [![CircleCI](https://circleci.com/gh/jpvantassel/hvsrpy.svg?style=svg)](https://circleci.com/gh/jpvantassel/hvsrpy)
 [![Documentation Status](https://readthedocs.org/projects/hvsrpy/badge/?version=latest)](https://hvsrpy.readthedocs.io/en/latest/?badge=latest)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/jpvantassel/hvsrpy.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/jpvantassel/hvsrpy/context:python)

docs/conf.py

@@ -22,7 +22,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'hvsrpy'
-copyright = '2019-2020, Joseph P. Vantassel'
+copyright = '2019-2021, Joseph P. Vantassel'
 author = 'Joseph P. Vantassel'
 
 # The full version, including alpha/beta/rc tags

docs/license.rst

@@ -3,7 +3,7 @@
 License Information
 ===================
 
-   Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+   Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by

examples/azimuthal_hvsrpy_interface.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This file is distributed as part of `hvsrpy`, a Python package for horizontal-to-vertical spectral ratio processing.\n",
     "\n",
-    "    Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
+    "    Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
     "\n",
     "    This program is free software: you can redistribute it and/or modify\n",
     "    it under the terms of the GNU General Public License as published by\n",
@@ -28,7 +28,7 @@
     "---\n",
     "\n",
     "`hvsrpy` is a Python package for performing horizontal-to-vertical spectral ratio\n",
-    "(H/V) processing. `hvsrpy` was developed by Joseph P. Vantassel  with\n",
+    "(HVSR) processing. `hvsrpy` was developed by Joseph P. Vantassel  with\n",
     "contributions from Dana M. Brannon under the supervision\n",
     "of Professor Brady R. Cox at The University of Texas at Austin.\n",
     "\n",
@@ -149,7 +149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## H/V Settings\n",
+    "## HVSR Settings\n",
     "---"
    ]
   },
@@ -172,9 +172,9 @@
     "# Maximum number of iterations to perform for rejection -> 50 is recommended.\n",
     "n_iteration = 50\n",
     "\n",
-    "# Distribution of f0 {\"lognormal\", \"normal\"}. Geopsy default \"normal\" -> \"log-normal\" is recommended.\n",
+    "# Distribution of f0 {\"lognormal\", \"normal\"}. Geopsy default \"normal\" -> \"lognormal\" is recommended.\n",
     "distribution_f0 = \"lognormal\"\n",
-    "# Distribution of mean curve {\"lognormal\", \"normal\"}. Geopsy default \"log-normal\" -> \"log-normal\" is recommended.\n",
+    "# Distribution of mean curve {\"lognormal\", \"normal\"}. Geopsy default \"lognormal\" -> \"lognormal\" is recommended.\n",
     "distribution_mc = \"lognormal\""
    ]
   },
@@ -213,7 +213,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Elapsed Time: 1.83 seconds\n",
+      "Elapsed Time: 2.03 seconds\n",
       "\n",
       "Statistics after rejection considering azimuth:\n"
      ]

examples/simple_hvsrpy_interface.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This file is distributed as part of `hvsrpy`, a Python package for horizontal-to-vertical spectral ratio processing.\n",
     "\n",
-    "    Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
+    "    Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
     "\n",
     "    This program is free software: you can redistribute it and/or modify\n",
     "    it under the terms of the GNU General Public License as published by\n",
@@ -28,7 +28,7 @@
     "---\n",
     "\n",
     "`hvsrpy` is a Python package for performing horizontal-to-vertical spectral ratio\n",
-    "(H/V) processing. `hvsrpy` was developed by Joseph P. Vantassel with\n",
+    "(HVSR) processing. `hvsrpy` was developed by Joseph P. Vantassel with\n",
     "contributions from Dana M. Brannon under the supervision of Professor Brady R.\n",
     "Cox at The University of Texas at Austin.\n",
     "\n",
@@ -158,7 +158,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## H/V Settings\n",
+    "## HVSR Settings\n",
     "---"
    ]
   },
@@ -182,9 +182,9 @@
     "# Maximum number of iterations to perform for rejection -> 50 is recommended.\n",
     "n_iteration = 50\n",
     "\n",
-    "# Distribution of f0 {\"lognormal\", \"normal\"}. Geopsy default \"normal\" -> \"log-normal\" is recommended.\n",
+    "# Distribution of f0 {\"lognormal\", \"normal\"}. Geopsy default \"normal\" -> \"lognormal\" is recommended.\n",
     "distribution_f0 = \"lognormal\"\n",
-    "# Distribution of mean curve {\"lognormal\", \"normal\"}. Geopsy default \"log-normal\" -> \"log-normal\" is recommended.\n",
+    "# Distribution of mean curve {\"lognormal\", \"normal\"}. Geopsy default \"lognormal\" -> \"lognormal\" is recommended.\n",
     "distribution_mc = \"lognormal\""
    ]
   },
@@ -223,7 +223,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Elapsed Time: 1.19 seconds\n",
+      "Elapsed Time: 0.36 seconds\n",
       "\n",
       "Statistics before rejection:\n"
      ]
@@ -500,12 +500,12 @@
     "\n",
     "            print(\"\\nAnalysis summary:\")  \n",
     "            display(pd.DataFrame(columns=[\"\"], index=[\"Window length\", \"No. of windows\", \"Number of iterations to convergence\", \"No. of rejected windows\"], \n",
-    "                    data=[f\"{windowlength}s\", str(sensor.ns.n_windows), f\"{c_iter} of {n_iteration} allowed\", str(sum(hv.rejected_window_indices))]))            \n",
+    "                    data=[f\"{windowlength}s\", str(sensor.ns.nseries), f\"{c_iter} of {n_iteration} allowed\", str(sum(hv.rejected_window_indices))]))            \n",
     "            print(\"\\nStatistics after rejection:\")\n",
     "            hv.print_stats(distribution_f0)\n",
     "    else:\n",
     "        display(pd.DataFrame(columns=[\"\"], index=[\"Window length\", \"No. of windows\"], \n",
-    "                         data=[f\"{windowlength}s\", str(sensor.ns.n_windows)]))\n",
+    "                         data=[f\"{windowlength}s\", str(sensor.ns.nseries)]))\n",
     "        hv.print_stats(distribution_f0)\n",
     "        fig.legend(loc=\"upper center\", bbox_to_anchor=(0.77, 0.4))\n",
     "        break\n",
@@ -518,7 +518,7 @@
     "    ax.plot(ctime.T, amp.T, linewidth=0.2, color='#888888')\n",
     "    ax.set_title(f\"Time Records ({name})\")\n",
     "    ax.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
-    "    ax.set_xlim(0, windowlength*timerecord.n_windows)\n",
+    "    ax.set_xlim(0, windowlength*timerecord.nseries)\n",
     "    ax.set_ylim(-1, 1)\n",
     "    ax.set_xlabel('Time (s)')\n",
     "    ax.set_ylabel('Normalized Amplitude')\n",

examples/spatial_hvsrpy_interface.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This file is distributed as part of `hvsrpy`, a Python package for horizontal-to-vertical spectral ratio processing.\n",
     "\n",
-    "    Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
+    "    Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)\n",
     "\n",
     "    This program is free software: you can redistribute it and/or modify\n",
     "    it under the terms of the GNU General Public License as published by\n",
@@ -378,21 +378,21 @@
        "    <tr>\n",
        "      <th>Fundamental Site Frequency, f0</th>\n",
        "      <td>0.58</td>\n",
-       "      <td>0.15</td>\n",
+       "      <td>0.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fundamental Site Period, T0</th>\n",
-       "      <td>1.73</td>\n",
-       "      <td>0.15</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>0.16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                Lognormal Median  Lognormal Standard Deviation\n",
-       "Fundamental Site Frequency, f0              0.58                          0.15\n",
-       "Fundamental Site Period, T0                 1.73                          0.15"
+       "Fundamental Site Frequency, f0              0.58                          0.16\n",
+       "Fundamental Site Period, T0                 1.74                          0.16"
       ]
      },
      "metadata": {},

hvsrpy/__init__.py

@@ -1,6 +1,6 @@
 # This file is part of hvsrpy a Python package for horizontal-to-vertical
 # spectral ratio processing.
-# Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+# Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 #
 #     This program is free software: you can redistribute it and/or modify
 #     it under the terms of the GNU General Public License as published by

hvsrpy/hvsr.py

@@ -1,6 +1,6 @@
 # This file is part of hvsrpy, a Python package for horizontal-to-vertical
 # spectral ratio processing.
-# Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+# Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 #
 #     This program is free software: you can redistribute it and/or modify
 #     it under the terms of the GNU General Public License as published by
@@ -40,7 +40,7 @@ class Hvsr():
         curve/time window and each column a frequency.
     frq : ndarray
         Vector of frequencies corresponds to each column.
-    n_windows : int
+    nseries : int
         Number of windows in `Hvsr` object.
     valid_window_indices : ndarray
         Boolean array indicating valid windows.
@@ -137,10 +137,10 @@ def __init__(self, amplitude, frequency, find_peaks=True,
             msg = f"Size of amplitude={self.amp.size} and frequency={self.frq.size} must be compatable."
             raise ValueError(msg)
 
-        self.n_windows = self.amp.shape[0]
-        self.valid_window_indices = np.ones(self.n_windows, dtype=bool)
-        self._main_peak_frq = np.zeros(self.n_windows)
-        self._main_peak_amp = np.zeros(self.n_windows)
+        self.nseries = self.amp.shape[0]
+        self.valid_window_indices = np.ones(self.nseries, dtype=bool)
+        self._main_peak_frq = np.zeros(self.nseries)
+        self._main_peak_amp = np.zeros(self.nseries)
 
         self.f_low = None if f_low is None else float(f_low) 
         if self.f_low is None:
@@ -233,7 +233,7 @@ def update_peaks(self, **kwargs):
         peak_indices, _ = self.find_peaks(self.amp[self.valid_window_indices, self.i_low:self.i_high],
                                           starting_index=self.i_low,
                                           **kwargs)
-        valid_indices = np.zeros(self.n_windows, dtype=bool)
+        valid_indices = np.zeros(self.nseries, dtype=bool)
         valid_count = 0
         for c_window, valid in enumerate(self.valid_window_indices):
             if not valid:
@@ -410,7 +410,7 @@ def std_curve(self, distribution='lognormal'):
             If `distribution` does not match the available options.
 
         """
-        if self.n_windows == 1:
+        if self.nseries == 1:
             msg = "The standard deviation of the mean curve is not defined for a single window."
             raise ValueError(msg)
         else:
@@ -508,7 +508,7 @@ def reject_windows(self, n=2, max_iterations=50,
             lower_bound = self.nstd_f0_frq(-n, distribution_f0)
             upper_bound = self.nstd_f0_frq(+n, distribution_f0)
 
-            valid_indices = np.zeros(self.n_windows, dtype=bool)
+            valid_indices = np.zeros(self.nseries, dtype=bool)
             for c_window, (c_valid, c_peak) in enumerate(zip(self.valid_window_indices, self._main_peak_frq)):
                 if not c_valid:
                     continue
@@ -687,6 +687,10 @@ def fclean(number, decimals=4):
 
     def _hvsrpy_style_lines(self, distribution_f0, distribution_mc):
         """Lines for hvsrpy-style file."""
+        # Correct distribution
+        distribution_f0 = self.correct_distribution(distribution_f0)
+        distribution_mc = self.correct_distribution(distribution_mc)
+
         # f0 from windows
         mean_f = self.mean_f0_frq(distribution_f0)
         sigm_f = self.std_f0_frq(distribution_f0)
@@ -700,7 +704,7 @@ def _hvsrpy_style_lines(self, distribution_f0, distribution_mc):
         _min = self.nstd_curve(-1, distribution_mc)
         _max = self.nstd_curve(+1, distribution_mc)
 
-        n_rejected = self.n_windows - sum(self.valid_window_indices)
+        n_rejected = self.nseries - sum(self.valid_window_indices)
         rejection = "False" if self.meta.get(
             'Performed Rejection') is None else "True"
         lines = [
@@ -709,12 +713,12 @@ def _hvsrpy_style_lines(self, distribution_f0, distribution_mc):
             f"# Method (),{self.meta.get('method')}",
             f"# Azimuth (),{self.meta.get('azimuth')}",
             f"# Window Length (s),{self.meta.get('Window Length')}",
-            f"# Total Number of Windows (),{self.n_windows}",
+            f"# Total Number of Windows (),{self.nseries}",
             f"# Frequency Domain Window Rejection Performed (),{rejection}",
             f"# Lower frequency limit for peaks (Hz),{self.f_low}",
             f"# Upper frequency limit for peaks (Hz),{self.f_high}",
             f"# Number of Standard Deviations Used for Rejection () [n],{self.meta.get('n')}",
-            f"# Number of Accepted Windows (),{self.n_windows-n_rejected}",
+            f"# Number of Accepted Windows (),{self.nseries-n_rejected}",
             f"# Number of Rejected Windows (),{n_rejected}",
             f"# Distribution of f0 (),{distribution_f0}"]
 

hvsrpy/hvsr_rotated.py

@@ -1,6 +1,6 @@
 # This file is part of hvsrpy, a Python package for horizontal-to-vertical
 # spectral ratio processing.
-# Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+# Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 #
 #     This program is free software: you can redistribute it and/or modify
 #     it under the terms of the GNU General Public License as published by
@@ -430,7 +430,7 @@ def print_stats(self, distribution_f0, places=2):  # pragma: no cover
 
     def _hvsrpy_style_lines(self, distribution_f0, distribution_mc):
         """Lines for hvsrpy-style file."""
-
+        # Correct distribution
         distribution_f0 = Hvsr.correct_distribution(distribution_f0)
         distribution_mc = Hvsr.correct_distribution(distribution_mc)
 
@@ -449,14 +449,14 @@ def _hvsrpy_style_lines(self, distribution_f0, distribution_mc):
 
         rejection = "False" if self.meta.get('Performed Rejection') is None else "True"
 
-        n_windows = self.hvsrs[0].n_windows
+        nseries = self.hvsrs[0].nseries
         n_accepted = sum([sum(hvsr.valid_window_indices) for hvsr in self.hvsrs])
-        n_rejected = self.azimuth_count*n_windows - n_accepted
+        n_rejected = self.azimuth_count*nseries - n_accepted
         lines = [
             f"# hvsrpy output version {__version__}",
             f"# File Name (),{self.meta.get('File Name')}",
             f"# Window Length (s),{self.meta.get('Window Length')}",
-            f"# Total Number of Windows per Azimuth (),{n_windows}",
+            f"# Total Number of Windows per Azimuth (),{nseries}",
             f"# Total Number of Azimuths (),{self.azimuth_count}",
             f"# Frequency Domain Window Rejection Performed (),{rejection}",
             f"# Lower frequency limit for peaks (Hz),{self.hvsrs[0].f_low}",
@@ -476,28 +476,28 @@ def fclean(number, decimals=4):
             ci_68_upper_t = np.exp(np.log(mean_t) + sigm_t)
 
             lines += [
-                f"# Median f0 (Hz) [LMf0,AZ],{fclean(mean_f)}",
-                f"# Lognormal standard deviation f0 () [SigmaLNf0,AZ],{fclean(sigm_f)}",
+                f"# Median f0 (Hz) [LMf0AZ],{fclean(mean_f)}",
+                f"# Lognormal standard deviation f0 () [SigmaLNf0AZ],{fclean(sigm_f)}",
                 f"# 68 % Confidence Interval f0 (Hz),{fclean(ci_68_lower_f)},to,{fclean(ci_68_upper_f)}",
-                f"# Median T0 (s) [LMT0,AZ],{fclean(mean_t)}",
-                f"# Lognormal standard deviation T0 () [SigmaLNT0,AZ],{fclean(sigm_t)}",
+                f"# Median T0 (s) [LMT0AZ],{fclean(mean_t)}",
+                f"# Lognormal standard deviation T0 () [SigmaLNT0AZ],{fclean(sigm_t)}",
                 f"# 68 % Confidence Interval T0 (s),{fclean(ci_68_lower_t)},to,{fclean(ci_68_upper_t)}",
             ]
 
         else:
             lines += [
-                f"# Mean f0 (Hz) [f0,AZ],{fclean(mean_f)}",
-                f"# Standard deviation f0 (Hz) [Sigmaf0,AZ],{fclean(sigm_f)}",
+                f"# Mean f0 (Hz) [f0AZ],{fclean(mean_f)}",
+                f"# Standard deviation f0 (Hz) [Sigmaf0AZ],{fclean(sigm_f)}",
                 f"# 68 % Confidence Interval f0 (Hz),{fclean(ci_68_lower_f)},to,{fclean(ci_68_upper_f)}",
-                f"# Mean T0 (s) [LMT0,AZ],NAN",
-                f"# Standard deviation T0 () [SigmaT0,AZ],NAN",
+                f"# Mean T0 (s) [LMT0AZ],NAN",
+                f"# Standard deviation T0 () [SigmaT0AZ],NAN",
                 f"# 68 % Confidence Interval T0 (s),NAN",
             ]
 
         c_type = "Median" if distribution_mc == "lognormal" else "Mean"
         lines += [
             f"# {c_type} Curve Distribution (),{distribution_mc}",
-            f"# {c_type} Curve Peak Frequency (Hz) [f0mc,AZ],{fclean(mc_peak_frq)}",
+            f"# {c_type} Curve Peak Frequency (Hz) [f0mcAZ],{fclean(mc_peak_frq)}",
             f"# {c_type} Curve Peak Amplitude (),{fclean(mc_peak_amp)}",
             f"# Frequency (Hz),{c_type} Curve,1 STD Below {c_type} Curve,1 STD Above {c_type} Curve",
         ]

hvsrpy/hvsr_spatial.py

@@ -1,6 +1,6 @@
 # This file is part of hvsrpy, a Python package for horizontal-to-vertical
 # spectral ratio processing.
-# Copyright (C) 2019-2020 Joseph P. Vantassel (jvantassel@utexas.edu)
+# Copyright (C) 2019-2021 Joseph P. Vantassel (jvantassel@utexas.edu)
 #
 #     This program is free software: you can redistribute it and/or modify
 #     it under the terms of the GNU General Public License as published by