final ruff format

VorTECHsa · Jun 20, 2024 · b4900b6 · b4900b6
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diff --git a/docs/examples/0_sample_load_cargo_movements.py b/docs/examples/0_sample_load_cargo_movements.py
@@ -12,6 +12,7 @@
 
 
 """
+
 from datetime import datetime
 
 from vortexasdk import CargoMovements

diff --git a/docs/examples/1_china.py b/docs/examples/1_china.py
@@ -12,6 +12,7 @@
 
 
 """
+
 from datetime import datetime
 
 from vortexasdk import CargoMovements, Geographies, Vessels

diff --git a/docs/examples/2_crude_from_saudi_arabia_to_india.py b/docs/examples/2_crude_from_saudi_arabia_to_india.py
@@ -5,6 +5,7 @@
  filtering on `Products: Crude` with `Origin: Saudi Arabia`, `Destination: India` and `Date Range: Departures in the last Month`.
 
 """
+
 from datetime import datetime
 
 from dateutil.relativedelta import relativedelta

diff --git a/docs/examples/3_chinese_daily_imports.py b/docs/examples/3_chinese_daily_imports.py
@@ -13,6 +13,7 @@
 ...
 
 """
+
 from datetime import datetime
 
 from vortexasdk import CargoTimeSeries, Geographies, Products

diff --git a/docs/examples/4_medium_sour_floating_storage.py b/docs/examples/4_medium_sour_floating_storage.py
@@ -13,6 +13,7 @@
 ...
 
 """
+
 from datetime import datetime
 
 from docs.utils import to_markdown

diff --git a/docs/examples/China_Flows.ipynb b/docs/examples/China_Flows.ipynb
diff --git a/docs/examples/Crude_Floating_Storage.ipynb b/docs/examples/Crude_Floating_Storage.ipynb
@@ -85,7 +85,7 @@
    "outputs": [],
    "source": [
     "# Defining date range for historical analysis\n",
-    "# NB: Vortexa data is currently available from 2016-01-01 with a maximum date range of 4 years per query \n",
+    "# NB: Vortexa data is currently available from 2016-01-01 with a maximum date range of 4 years per query\n",
     "START_DATE = datetime(2016, 1, 31)\n",
     "END_DATE = datetime(2020, 1, 31)\n",
     "DATE_RANGE = pd.date_range(START_DATE, END_DATE)\n",
@@ -107,30 +107,45 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def fetch_global_crude_floating_storage_timeseries(start_date, end_date, unit=\"t\"):\n",
-    "    \n",
+    "def fetch_global_crude_floating_storage_timeseries(\n",
+    "    start_date, end_date, unit=\"t\"\n",
+    "):\n",
     "    # Find Crude/Condensates ID, ensuring its uniqueness\n",
-    "    crude_and_condensates = [p.id for p in Products().search(\"crude\").to_list() if p.name==\"Crude/Condensates\"]\n",
+    "    crude_and_condensates = [\n",
+    "        p.id\n",
+    "        for p in Products().search(\"crude\").to_list()\n",
+    "        if p.name == \"Crude/Condensates\"\n",
+    "    ]\n",
     "    assert len(crude_and_condensates) == 1\n",
-    "    \n",
+    "\n",
     "    # make Vortexa API query\n",
     "    # NB: disable_geographic_exclusion_rules is set to True to include intra-movements\n",
-    "    df_fs = CargoTimeSeries().search(timeseries_frequency=\"day\",\n",
-    "                                     timeseries_unit=unit,\n",
-    "                                     disable_geographic_exclusion_rules=True,\n",
-    "                                     filter_products=crude_and_condensates,\n",
-    "                                     filter_activity=\"storing_state\",\n",
-    "                                     filter_time_min=start_date,\n",
-    "                                     filter_time_max=end_date).to_df()\n",
+    "    df_fs = (\n",
+    "        CargoTimeSeries()\n",
+    "        .search(\n",
+    "            timeseries_frequency=\"day\",\n",
+    "            timeseries_unit=unit,\n",
+    "            disable_geographic_exclusion_rules=True,\n",
+    "            filter_products=crude_and_condensates,\n",
+    "            filter_activity=\"storing_state\",\n",
+    "            filter_time_min=start_date,\n",
+    "            filter_time_max=end_date,\n",
+    "        )\n",
+    "        .to_df()\n",
+    "    )\n",
     "\n",
     "    # rename columns\n",
-    "    df_fs = df_fs.rename(columns={\"key\": \"date\",\n",
-    "                                  \"value\": unit,\n",
-    "                                  \"count\": \"number_of_cargo_movements\"})\n",
-    "    \n",
+    "    df_fs = df_fs.rename(\n",
+    "        columns={\n",
+    "            \"key\": \"date\",\n",
+    "            \"value\": unit,\n",
+    "            \"count\": \"number_of_cargo_movements\",\n",
+    "        }\n",
+    "    )\n",
+    "\n",
     "    # remove time zone from timestamp\n",
     "    df_fs[\"date\"] = pd.to_datetime(df_fs[\"date\"]).dt.tz_localize(None)\n",
-    "    \n",
+    "\n",
     "    return df_fs"
    ]
   },
@@ -277,7 +292,9 @@
     }
    ],
    "source": [
-    "df_fs = fetch_global_crude_floating_storage_timeseries(START_DATE, END_DATE, UNIT)\n",
+    "df_fs = fetch_global_crude_floating_storage_timeseries(\n",
+    "    START_DATE, END_DATE, UNIT\n",
+    ")\n",
     "df_fs"
    ]
   },
@@ -461,8 +478,16 @@
     }
    ],
    "source": [
-    "spot_prices = pd.read_excel(\"https://www.eia.gov/dnav/pet/xls/PET_PRI_SPT_S1_D.xls\", sheet_name=\"Data 1\", skiprows=[0,1])\n",
-    "spot_prices = spot_prices.set_index(\"Date\").fillna(method=\"ffill\").reindex(DATE_RANGE, method=\"ffill\")\n",
+    "spot_prices = pd.read_excel(\n",
+    "    \"https://www.eia.gov/dnav/pet/xls/PET_PRI_SPT_S1_D.xls\",\n",
+    "    sheet_name=\"Data 1\",\n",
+    "    skiprows=[0, 1],\n",
+    ")\n",
+    "spot_prices = (\n",
+    "    spot_prices.set_index(\"Date\")\n",
+    "    .fillna(method=\"ffill\")\n",
+    "    .reindex(DATE_RANGE, method=\"ffill\")\n",
+    ")\n",
     "spot_prices"
    ]
   },
@@ -697,8 +722,16 @@
     }
    ],
    "source": [
-    "future_prices = pd.read_excel(\"https://www.eia.gov/dnav/pet/xls/PET_PRI_FUT_S1_D.xls\", sheet_name=\"Data 1\", skiprows=[0,1])\n",
-    "future_prices = future_prices.set_index(\"Date\").fillna(method=\"ffill\").reindex(DATE_RANGE, method=\"ffill\")\n",
+    "future_prices = pd.read_excel(\n",
+    "    \"https://www.eia.gov/dnav/pet/xls/PET_PRI_FUT_S1_D.xls\",\n",
+    "    sheet_name=\"Data 1\",\n",
+    "    skiprows=[0, 1],\n",
+    ")\n",
+    "future_prices = (\n",
+    "    future_prices.set_index(\"Date\")\n",
+    "    .fillna(method=\"ffill\")\n",
+    "    .reindex(DATE_RANGE, method=\"ffill\")\n",
+    ")\n",
     "future_prices"
    ]
   },
@@ -947,7 +980,9 @@
     }
    ],
    "source": [
-    "calendar_spread.plot(title=\"Spread between Future and Spot crude oil prices\", grid=True)\n",
+    "calendar_spread.plot(\n",
+    "    title=\"Spread between Future and Spot crude oil prices\", grid=True\n",
+    ")\n",
     "plt.xlabel(\"date\")\n",
     "plt.ylabel(\"USD\");"
    ]
@@ -1000,7 +1035,9 @@
    "outputs": [],
    "source": [
     "def crosscorr(series_x, series_y, lags):\n",
-    "    return pd.Series([series_y.corr(series_x.shift(lag)) for lag in lags], index=lags)"
+    "    return pd.Series(\n",
+    "        [series_y.corr(series_x.shift(lag)) for lag in lags], index=lags\n",
+    "    )"
    ]
   },
   {
@@ -1048,7 +1085,7 @@
    "outputs": [],
    "source": [
     "def plot_crosscorr(series_x, series_y, maxlag, label_x, label_y):\n",
-    "    lags = np.arange(0, maxlag+1)\n",
+    "    lags = np.arange(0, maxlag + 1)\n",
     "\n",
     "    plt.subplot(\"211\")\n",
     "    xcorr_x_y = crosscorr(series_x, series_y, lags)\n",
@@ -1088,7 +1125,13 @@
     }
    ],
    "source": [
-    "plot_crosscorr(calendar_spread, floating_storage, maxlag=MAXLAG, label_x=\"calendar spread\", label_y=\"floating storage\")"
+    "plot_crosscorr(\n",
+    "    calendar_spread,\n",
+    "    floating_storage,\n",
+    "    maxlag=MAXLAG,\n",
+    "    label_x=\"calendar spread\",\n",
+    "    label_y=\"floating storage\",\n",
+    ")"
    ]
   },
   {
@@ -1119,7 +1162,13 @@
     }
    ],
    "source": [
-    "plot_crosscorr(spot_prices, floating_storage, maxlag=MAXLAG, label_x=\"spot prices\", label_y=\"floating storage\")"
+    "plot_crosscorr(\n",
+    "    spot_prices,\n",
+    "    floating_storage,\n",
+    "    maxlag=MAXLAG,\n",
+    "    label_x=\"spot prices\",\n",
+    "    label_y=\"floating storage\",\n",
+    ")"
    ]
   },
   {
@@ -1152,7 +1201,7 @@
     }
    ],
    "source": [
-    "lags = np.arange(0, MAXLAG+1)\n",
+    "lags = np.arange(0, MAXLAG + 1)\n",
     "\n",
     "crosscorr(floating_storage, floating_storage, lags).plot()\n",
     "crosscorr(calendar_spread, calendar_spread, lags).plot()\n",
@@ -1214,7 +1263,11 @@
     }
    ],
    "source": [
-    "gct = grangercausalitytests(pd.concat([floating_storage, calendar_spread], axis=1), maxlag=2, verbose=True)"
+    "gct = grangercausalitytests(\n",
+    "    pd.concat([floating_storage, calendar_spread], axis=1),\n",
+    "    maxlag=2,\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -1233,21 +1286,37 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_granger_pvalues(series_x, series_y, maxlag, label_x, label_y, test=\"ssr_ftest\", confidence_level=0.05):\n",
-    "    lags = np.arange(1, maxlag+1)\n",
+    "def plot_granger_pvalues(\n",
+    "    series_x,\n",
+    "    series_y,\n",
+    "    maxlag,\n",
+    "    label_x,\n",
+    "    label_y,\n",
+    "    test=\"ssr_ftest\",\n",
+    "    confidence_level=0.05,\n",
+    "):\n",
+    "    lags = np.arange(1, maxlag + 1)\n",
     "\n",
     "    plt.subplot(\"211\")\n",
-    "    gct_x_y = grangercausalitytests(pd.concat([series_y, series_x], axis=1), maxlag=maxlag, verbose=False)\n",
-    "    pvalue_x_y = pd.Series([gct_x_y[lag][0][test][1] for lag in lags], index=lags)\n",
+    "    gct_x_y = grangercausalitytests(\n",
+    "        pd.concat([series_y, series_x], axis=1), maxlag=maxlag, verbose=False\n",
+    "    )\n",
+    "    pvalue_x_y = pd.Series(\n",
+    "        [gct_x_y[lag][0][test][1] for lag in lags], index=lags\n",
+    "    )\n",
     "    pvalue_x_y.plot(title=f\"{label_x} -> {label_y}\", grid=True)\n",
-    "    plt.plot((0, maxlag),(confidence_level, confidence_level),\"--r\")\n",
+    "    plt.plot((0, maxlag), (confidence_level, confidence_level), \"--r\")\n",
     "    plt.ylabel(\"p-value\")\n",
     "\n",
     "    plt.subplot(\"210\")\n",
-    "    gct_y_x = grangercausalitytests(pd.concat([series_x, series_y], axis=1), maxlag=maxlag, verbose=False)\n",
-    "    pvalue_y_x = pd.Series([gct_y_x[lag][0][test][1] for lag in lags], index=lags)\n",
+    "    gct_y_x = grangercausalitytests(\n",
+    "        pd.concat([series_x, series_y], axis=1), maxlag=maxlag, verbose=False\n",
+    "    )\n",
+    "    pvalue_y_x = pd.Series(\n",
+    "        [gct_y_x[lag][0][test][1] for lag in lags], index=lags\n",
+    "    )\n",
     "    pvalue_y_x.plot(title=f\"{label_y} -> {label_x}\", grid=True)\n",
-    "    plt.plot((0, maxlag),(confidence_level, confidence_level),\"--r\")\n",
+    "    plt.plot((0, maxlag), (confidence_level, confidence_level), \"--r\")\n",
     "    plt.xlabel(\"lag [days]\")\n",
     "    plt.ylabel(\"p-value\")"
    ]
@@ -1278,7 +1347,13 @@
     }
    ],
    "source": [
-    "plot_granger_pvalues(calendar_spread, floating_storage, maxlag=MAXLAG, label_x=\"calendar spread\", label_y=\"floating storage\")"
+    "plot_granger_pvalues(\n",
+    "    calendar_spread,\n",
+    "    floating_storage,\n",
+    "    maxlag=MAXLAG,\n",
+    "    label_x=\"calendar spread\",\n",
+    "    label_y=\"floating storage\",\n",
+    ")"
    ]
   },
   {
@@ -1311,7 +1386,13 @@
     }
    ],
    "source": [
-    "plot_granger_pvalues(spot_prices, floating_storage, maxlag=MAXLAG, label_x=\"spot prices\", label_y=\"floating storage\")"
+    "plot_granger_pvalues(\n",
+    "    spot_prices,\n",
+    "    floating_storage,\n",
+    "    maxlag=MAXLAG,\n",
+    "    label_x=\"spot prices\",\n",
+    "    label_y=\"floating storage\",\n",
+    ")"
    ]
   },
   {
@@ -1342,7 +1423,13 @@
     }
    ],
    "source": [
-    "plot_granger_pvalues(spot_prices, calendar_spread, maxlag=MAXLAG, label_x=\"spot prices\", label_y=\"calendar spread\")"
+    "plot_granger_pvalues(\n",
+    "    spot_prices,\n",
+    "    calendar_spread,\n",
+    "    maxlag=MAXLAG,\n",
+    "    label_x=\"spot prices\",\n",
+    "    label_y=\"calendar spread\",\n",
+    ")"
    ]
   },
   {
@@ -1399,10 +1486,14 @@
     "end_ts = pd.Timestamp(\"now\")\n",
     "start_ts = end_ts - pd.Timedelta(\"100 d\")\n",
     "\n",
-    "df_fs_now = fetch_global_crude_floating_storage_timeseries(start_ts.date(), end_ts.date(), UNIT)\n",
+    "df_fs_now = fetch_global_crude_floating_storage_timeseries(\n",
+    "    start_ts.date(), end_ts.date(), UNIT\n",
+    ")\n",
     "\n",
     "floating_storage_now = df_fs_now.set_index(\"date\")[UNIT] / 1000\n",
-    "floating_storage_now.plot(title=\"Global crude oil floating storage - last 100 days\", grid=True)\n",
+    "floating_storage_now.plot(\n",
+    "    title=\"Global crude oil floating storage - last 100 days\", grid=True\n",
+    ")\n",
     "plt.xlabel(\"date\")\n",
     "plt.ylabel(\"k\" + UNIT);"
    ]
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,6 +12,7 @@


		"""

		from datetime import datetime

		from vortexasdk import CargoMovements
Expand Down
-Original file line number
+Diff line change
@@ Expand Up / @@ -5,6 +5,7 @@ @@
      filtering on `Products: Crude` with `Origin: Saudi Arabia`, `Destination: India` and `Date Range: Departures in the last Month`.
     """
     from datetime import datetime
     from dateutil.relativedelta import relativedelta
@@ Expand Down @@