Merge pull request #18 from jpn--/develop

xarray accessors

.github/workflows/dev-docs.yml

@@ -59,18 +59,10 @@ jobs:
       run: |
         conda info -a
         conda list
-    - name: Cache documentation html
-      id: cachedocs
-      uses: actions/cache@v2
-      with:
-        # npm cache files are stored in `~/.npm` on Linux/macOS
-        path: larch/book/_build/html
-        key: ${{ runner.os }}-docs-html-${{ env.LARCH_VERSION }} }}
     - name: Build the docs
-      if: steps.cachedocs.outputs.cache-hit != 'true'
       run: |
         python larch/book/_scripts/hide_test_cells.py
-        python larch/book/_scripts/developer_doc_title.py ${{ env.LARCH_VERSION }}
+        python larch/book/_scripts/developer_doc_title.py v${{ env.LARCH_VERSION }}
         jb build larch/book
     - name: Push to GitHub Pages
       uses: peaceiris/actions-gh-pages@v3.8.0

.gitignore

@@ -71,3 +71,6 @@ latent-class-example-report.html
 doc/build
 sandbox.py
 /LARCH_VERSION.txt
+exampville_dest_choice.html
+exampville_mode_choice.html
+logsums.zarr.zip

book/_config.yml

@@ -1,7 +1,7 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: "v5.6.1"
+title: "v5.7.0"
 author: Jeffrey Newman
 logo: img/larch-logo.png
 
@@ -19,7 +19,7 @@ execute:
 
 # Add a bibtex file so that we can create citations
 bibtex_bibfiles:
-  - references.bib
+- references.bib
 
 # Information about where the book exists on the web
 repository:
@@ -36,21 +36,21 @@ html:
 
 sphinx:
   extra_extensions:
-    - 'sphinx.ext.autodoc'
-    - 'sphinx.ext.autosummary'
-    - 'sphinx.ext.napoleon'
-    - 'sphinx.ext.mathjax'
-    - 'sphinx.ext.intersphinx'
-    - 'sphinx.ext.doctest'
-    - 'sphinx.ext.graphviz'
-    - 'sphinx.ext.viewcode'
+  - sphinx.ext.autodoc
+  - sphinx.ext.autosummary
+  - sphinx.ext.napoleon
+  - sphinx.ext.mathjax
+  - sphinx.ext.intersphinx
+  - sphinx.ext.doctest
+  - sphinx.ext.graphviz
+  - sphinx.ext.viewcode
   mathjax_config:
     TeX:
       Macros:
-        "N": "\\mathbb{N}"
-        "floor": [ "\\lfloor#1\\rfloor", 1 ]
-        "bmat": [ "\\left[\\begin{array}" ]
-        "emat": [ "\\end{array}\\right]" ]
+        N: \mathbb{N}
+        floor: [\lfloor#1\rfloor, 1]
+        bmat: ['\left[\begin{array}']
+        emat: ['\end{array}\right]']
   latex_elements:
     preamble: |
       \newcommand\N{\mathbb{N}}
@@ -59,27 +59,27 @@ sphinx:
       \newcommand{\emat}{\end{array}\right]}
   intersphinx_mapping:
     pandas:
-      - "http://pandas.pydata.org/pandas-docs/stable/"
-      - null
+    - http://pandas.pydata.org/pandas-docs/stable/
+    -
     sklearn:
-      - "https://scikit-learn.org/stable/"
-      - null
+    - https://scikit-learn.org/stable/
+    -
     networkx:
-      - "https://networkx.github.io/documentation/latest/"
-      - null
+    - https://networkx.github.io/documentation/latest/
+    -
     conda:
-      - "https://docs.conda.io/projects/conda/en/latest/"
-      - null
+    - https://docs.conda.io/projects/conda/en/latest/
+    -
     xarray:
-      - "https://xarray.pydata.org/en/stable/"
-      - null
+    - https://xarray.pydata.org/en/stable/
+    -
   config:
     bibtex_reference_style: author_year
-    autosummary_generate: True
+    autosummary_generate: true
     autodoc_default_options:
-      show-inheritance: True
-    add_module_names: False
-    napoleon_use_rtype: False
+      show-inheritance: true
+    add_module_names: false
+    napoleon_use_rtype: false
     rst_epilog: |
       .. |idca| replace:: :ref:`idca <idca>`
       .. |idco| replace:: :ref:`idco <idco>`
@@ -97,8 +97,8 @@ sphinx:
         sizes: 32x32
         href: img/larch_favicon.png
       switcher:
-        json_url: "https://larch.newman.me/_static/switcher.json"
-        url_template: "https://larch.newman.me/v{version}/"
-        version_match: "5.6.1"
+        json_url: https://larch.newman.me/_static/switcher.json
+        url_template: https://larch.newman.me/v{version}/
+        version_match: 5.7.0
       navbar_end:
       - version-switcher

book/_static/switcher.json

@@ -1,7 +1,7 @@
 [
     {
-        "name": "v5.6.1 (latest)",
-        "version": "5.6.1"
+        "name": "v5.7.0 (latest)",
+        "version": "5.7.0"
     },
     {
         "version": "5.4.1"

book/api/dataset.rst

@@ -53,6 +53,7 @@ Methods
     Dataset.caseids
     Dataset.dissolve_zero_variance
     Dataset.query_cases
+    Dataset.set_altids
     Dataset.set_altnames
     Dataset.set_dtypes
     Dataset.setup_flow

book/example/000_mtc_data.ipynb

@@ -107,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = lx.Dataset.from_idca(df)\n",
+    "ds = lx.Dataset.construct.from_idca(df)\n",
     "ds"
    ]
   },
@@ -141,8 +141,8 @@
     "assert ds['ivtt'].dims == ('casenum','altnum')\n",
     "assert ds['ivtt'].dtype.kind == 'f'\n",
     "assert ds.dims == {'casenum': 5029, 'altnum': 6}\n",
-    "assert ds.CASEID == 'casenum'\n",
-    "assert ds.ALTID == 'altnum'"
+    "assert ds.dc.CASEID == 'casenum'\n",
+    "assert ds.dc.ALTID == 'altnum'"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = ds.set_altnames({\n",
+    "ds = ds.dc.set_altnames({\n",
     "    1:'DA', 2:'SR2', 3:'SR3+', 4:'Transit', 5:'Bike', 6:'Walk',\n",
     "})\n",
     "ds"

book/example/017A_mnl_segmented.ipynb

@@ -70,8 +70,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d1 = d.query_cases(\"numveh <= 1\")\n",
-    "d2 = d.query_cases(\"numveh > 1\")\n",
+    "d1 = d.dc.query_cases(\"numveh <= 1\")\n",
+    "d2 = d.dc.query_cases(\"numveh > 1\")\n",
     "m1 = lx.Model(d1, title=\"Cars<=1\")\n",
     "m2 = lx.Model(d2, title=\"Cars>=2\")"
    ]
@@ -296,7 +296,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8aec3d88",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "# TEST\n",
@@ -341,7 +345,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "157e18c6",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "# TEST\n",
@@ -378,13 +386,18 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a308716f",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "# TEST\n",
-    "import re\n",
+    "import re, platform\n",
     "mash = lambda x: re.sub('\\\\s+', ' ', str(x)).strip()\n",
-    "assert mash(mg.parameter_summary().data) == mash('''\n",
+    "if platform.system() != \"Windows\":\n",
+    "    assert mash(mg.parameter_summary().data) == mash('''\n",
     "                                        Value    Std Err  t Stat Signif  Null Value\n",
     "Category  Parameter                                                                \n",
     "LOS       costbyincome                -0.0174     0.0117   -1.49                0.0\n",

book/example/017_mnl_final.ipynb

@@ -347,7 +347,7 @@
     "df = pd.read_csv(lx.example_file(\"MTCwork.csv.gz\"), index_col=['casenum','altnum'])\n",
     "df.index = df.index.rename('altid', level=1)\n",
     "df['altnum'] = df.index.get_level_values(1)\n",
-    "m.datatree = lx.Dataset.from_idce(df)\n",
+    "m.datatree = lx.Dataset.construct.from_idce(df)\n",
     "m.availability_var = '1'\n",
     "assert m.loglike() == approx(-3444.185105027836)\n",
     "assert m.n_cases == 5029\n",

book/example/022-nl.ipynb

@@ -141,7 +141,7 @@
     "    'wkempden_SR3+': 0.0016377373877722236,\n",
     "    'wkempden_Transit': 0.0022365960915132523,\n",
     "    'wkempden_Walk': 0.0021703617647683077,\n",
-    "})"
+    "}, rel=1e-3)"
    ]
   },
   {
@@ -197,7 +197,7 @@
     "    'wkempden_Transit': 4.408676458615664,\n",
     "    'wkempden_Walk': 2.8469911145512783\n",
     "}, name='t_stat')\n",
-    "pd.testing.assert_series_equal(m.pf.t_stat, expected_t)"
+    "pd.testing.assert_series_equal(m.pf.t_stat, expected_t, rtol=5.0e-2)"
    ]
   }
  ],

book/example/028-nl.ipynb

@@ -132,7 +132,7 @@
     "    'wkempden_SR3+': 0.0014014004179079879,\n",
     "    'wkempden_Transit': 0.002309868017949087,\n",
     "    'wkempden_Walk': 0.0022363349074798343\n",
-    "})"
+    "}, rel=1e-3)"
    ]
   },
   {
@@ -189,7 +189,7 @@
     "    'wkempden_Transit': 4.423243628445124,\n",
     "    'wkempden_Walk': 2.912974554332499,\n",
     "}, name='t_stat')\n",
-    "pd.testing.assert_series_equal(m.pf.t_stat, expected_t)"
+    "pd.testing.assert_series_equal(m.pf.t_stat, expected_t, rtol=5.0e-2)"
    ]
   }
  ],

book/example/101_swissmetro_mnl.ipynb

@@ -237,7 +237,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = lx.Dataset.from_idco(selected_data, alts={1:'Train', 2:'SM', 3:'Car'})"
+    "ds = lx.Dataset.construct.from_idco(selected_data, alts={1:'Train', 2:'SM', 3:'Car'})"
    ]
   },
   {
@@ -332,7 +332,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b06b8f7d",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "# TEST\n",

book/example/102-swissmetro-weighted.ipynb

@@ -58,7 +58,7 @@
    "outputs": [],
    "source": [
     "raw_data = pd.read_csv(lx.example_file('swissmetro.csv.gz')).rename_axis(index='CASEID')\n",
-    "data = lx.Dataset.from_idco(raw_data, alts={1:'Train', 2:'SM', 3:'Car'})\n",
+    "data = lx.Dataset.construct.from_idco(raw_data, alts={1:'Train', 2:'SM', 3:'Car'})\n",
     "data"
    ]
   },
@@ -78,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "m = lx.Model(data.query_cases(\"PURPOSE in (1,3) and CHOICE != 0\"))"
+    "m = lx.Model(data.dc.query_cases(\"PURPOSE in (1,3) and CHOICE != 0\"))"
    ]
   },
   {
@@ -257,7 +257,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "b4f540ea",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
    "outputs": [],
    "source": [
     "# TEST\n",

book/example/109-swissmetro-nl.ipynb

@@ -58,8 +58,8 @@
    "outputs": [],
    "source": [
     "raw_data = pd.read_csv(lx.example_file('swissmetro.csv.gz')).rename_axis(index='CASEID')\n",
-    "data = lx.Dataset.from_idco(raw_data, alts={1:'Train', 2:'SM', 3:'Car'})\n",
-    "m = lx.Model(data.query_cases(\"PURPOSE in (1,3) and CHOICE != 0\"))"
+    "data = lx.Dataset.construct.from_idco(raw_data, alts={1:'Train', 2:'SM', 3:'Car'})\n",
+    "m = lx.Model(data.dc.query_cases(\"PURPOSE in (1,3) and CHOICE != 0\"))"
    ]
   },
   {